Triple
T23269436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DAMA/LIBRA |
E588247
|
entity |
| Predicate | predecessor |
P97
|
FINISHED |
| Object |
DAMA/NaI
DAMA/NaI was an underground sodium iodide scintillator experiment at Italy’s Gran Sasso laboratory that searched for dark matter via annual modulation signals.
|
E588247
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: DAMA/NaI | Statement: [DAMA/LIBRA, predecessor, DAMA/NaI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DAMA/NaI Context triple: [DAMA/LIBRA, predecessor, DAMA/NaI]
-
A.
DAMA/LIBRA
DAMA/LIBRA is a dark matter direct-detection experiment that uses highly radiopure sodium iodide scintillators to search for an annual modulation signal in underground measurements.
-
B.
DAM
DAM is a Frankfurt-based museum dedicated to the history, theory, and contemporary practice of architecture in Germany and beyond.
-
C.
DAM
DAM is the National Rail station code used to identify Dalmeny railway station in Scotland’s rail network.
-
D.
DAM
DAM is the three-letter IATA airport code for Damascus International Airport, the main airport serving Syria’s capital city.
-
E.
NSMIA
NSMIA is a U.S. federal law enacted in 1996 that reallocated regulatory authority between federal and state governments over securities offerings and investment advisers to streamline and modernize securities regulation.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: DAMA/NaI Triple: [DAMA/LIBRA, predecessor, DAMA/NaI]
Generated description
DAMA/NaI was an underground sodium iodide scintillator experiment at Italy’s Gran Sasso laboratory that searched for dark matter via annual modulation signals.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DAMA/NaI Target entity description: DAMA/NaI was an underground sodium iodide scintillator experiment at Italy’s Gran Sasso laboratory that searched for dark matter via annual modulation signals.
-
A.
DAMA/LIBRA
chosen
DAMA/LIBRA is a dark matter direct-detection experiment that uses highly radiopure sodium iodide scintillators to search for an annual modulation signal in underground measurements.
-
B.
DAM
DAM is a Frankfurt-based museum dedicated to the history, theory, and contemporary practice of architecture in Germany and beyond.
-
C.
DAM
DAM is the three-letter IATA airport code for Damascus International Airport, the main airport serving Syria’s capital city.
-
D.
DAM
DAM is the National Rail station code used to identify Dalmeny railway station in Scotland’s rail network.
-
E.
NSMIA
NSMIA is a U.S. federal law enacted in 1996 that reallocated regulatory authority between federal and state governments over securities offerings and investment advisers to streamline and modernize securities regulation.
- F. None of above.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e25d148adc819088efbf42672604e9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1957219188190b30bceffad1542da |
completed | April 29, 2026, 5:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c3f7797a481909c56589d1d3292ea |
completed | May 19, 2026, 10:46 a.m. |
| NEDg | Description generation | batch_6a0c40297d3c8190b366b60cec3dbbf8 |
completed | May 19, 2026, 10:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c40ffd67481908931ffc2d91fb02b |
completed | May 19, 2026, 10:52 a.m. |
Created at: April 17, 2026, 4:45 p.m.