Triple
T22446199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lars Bak |
E554868
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object |
OOVM
OOVM is a software company associated with Danish engineer Lars Bak, known for his work on high-performance virtual machines and programming language runtimes.
|
E1536600
|
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: OOVM | Statement: [Lars Bak, employer, OOVM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OOVM Context triple: [Lars Bak, employer, OOVM]
-
A.
OVSM
OVSM is the volcanological and seismological observatory responsible for monitoring volcanic and earthquake activity in Martinique.
-
B.
OMVS
OMVS is the multinational organization responsible for managing and developing the shared water resources and infrastructure of the Senegal River basin.
-
C.
OOA
OOA is the IATA airport code for Oskaloosa Municipal Airport, a public airport serving Oskaloosa, Iowa, in the United States.
-
D.
OVP
OVP is the government office that supports and carries out the official duties and functions of the Vice President of the Philippines.
-
E.
VOMM
VOMM is the ICAO airport code for Chennai International Airport, a major aviation hub in southern India.
- 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: OOVM Triple: [Lars Bak, employer, OOVM]
Generated description
OOVM is a software company associated with Danish engineer Lars Bak, known for his work on high-performance virtual machines and programming language runtimes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: OOVM Target entity description: OOVM is a software company associated with Danish engineer Lars Bak, known for his work on high-performance virtual machines and programming language runtimes.
-
A.
OVSM
OVSM is the volcanological and seismological observatory responsible for monitoring volcanic and earthquake activity in Martinique.
-
B.
OMVS
OMVS is the multinational organization responsible for managing and developing the shared water resources and infrastructure of the Senegal River basin.
-
C.
OOA
OOA is the IATA airport code for Oskaloosa Municipal Airport, a public airport serving Oskaloosa, Iowa, in the United States.
-
D.
OVP
OVP is the government office that supports and carries out the official duties and functions of the Vice President of the Philippines.
-
E.
VOMM
VOMM is the ICAO airport code for Chennai International Airport, a major aviation hub in southern India.
- F. None of above. chosen
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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b4803908190990280ebd258cb03 |
completed | April 29, 2026, 1:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b0c7640fc8190a2dd1643a7c2a5b0 |
completed | May 18, 2026, 12:56 p.m. |
| NEDg | Description generation | batch_6a0b0cd3f7bc8190bdc1e241ea1c642f |
completed | May 18, 2026, 12:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b0d40e0c8819083a430e30dba1848 |
completed | May 18, 2026, 12:59 p.m. |
Created at: April 16, 2026, 8:47 p.m.