Triple
T21840753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European meteorological services |
E539244
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
DMI
DMI is Denmark’s national meteorological institute, responsible for weather forecasting, climate monitoring, and related services for the country and surrounding regions.
|
E1503416
|
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: DMI | Statement: [European meteorological services, hasMember, DMI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DMI Context triple: [European meteorological services, hasMember, DMI]
-
A.
DIM
DIM is the commonly used abbreviation for Deportivo Independiente Medellín, a professional football club based in Medellín, Colombia.
-
B.
DIM
DIM is a French clothing brand best known for its innovative and stylish underwear, hosiery, and lingerie.
-
C.
D.V.I.
D.V.I. is the standard abbreviation for the District Court of the Virgin Islands, a federal court with jurisdiction over the U.S. Virgin Islands.
-
D.
DSI
DSI is the Deutsches SOFIA Institut, a German research institute dedicated to supporting and conducting astronomical observations with the SOFIA airborne observatory.
-
E.
DSI
DSI is the IATA airport code for Destin Executive Airport, a public airport serving Destin, Florida.
- 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: DMI Triple: [European meteorological services, hasMember, DMI]
Generated description
DMI is Denmark’s national meteorological institute, responsible for weather forecasting, climate monitoring, and related services for the country and surrounding regions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DMI Target entity description: DMI is Denmark’s national meteorological institute, responsible for weather forecasting, climate monitoring, and related services for the country and surrounding regions.
-
A.
DIM
DIM is a French clothing brand best known for its innovative and stylish underwear, hosiery, and lingerie.
-
B.
DIM
DIM is the commonly used abbreviation for Deportivo Independiente Medellín, a professional football club based in Medellín, Colombia.
-
C.
D.V.I.
D.V.I. is the standard abbreviation for the District Court of the Virgin Islands, a federal court with jurisdiction over the U.S. Virgin Islands.
-
D.
DSI
DSI is the Deutsches SOFIA Institut, a German research institute dedicated to supporting and conducting astronomical observations with the SOFIA airborne observatory.
-
E.
DSI
DSI is the IATA airport code for Destin Executive Airport, a public airport serving Destin, Florida.
- 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_69e0c476c3c88190a92d08ebb59a128a |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0a7ab71e081908e3d3293743e6409 |
completed | April 28, 2026, 12:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a45f0ded08190b17f3473ac339b01 |
completed | May 17, 2026, 10:49 p.m. |
| NEDg | Description generation | batch_6a0a4682eca08190916e211b0e9e3861 |
completed | May 17, 2026, 10:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a474e900c81909c5167c63509ae0a |
completed | May 17, 2026, 10:55 p.m. |
Created at: April 16, 2026, 6:55 p.m.