Triple
T6513836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pierrefonds Airport |
E148202
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object |
FMEP
FMEP is the ICAO airport code for Pierrefonds Airport on Réunion Island in the Indian Ocean.
|
E601390
|
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: FMEP | Statement: [Pierrefonds Airport, ICAOcode, FMEP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FMEP Context triple: [Pierrefonds Airport, ICAOcode, FMEP]
-
A.
EFPM
EFPM is the Executive Fellow Programme in Management, a doctoral-level program designed for working professionals to pursue advanced research in management.
-
B.
MEF
MEF is Italy’s Ministry of Economy and Finance, the government department responsible for national economic policy, public finances, and the state budget.
-
C.
MPEA
MPEA is the commonly used abbreviation for the Metropolitan Pier and Exposition Authority, the public agency that owns and operates Chicago’s McCormick Place convention center and Navy Pier.
-
D.
FPM
FPM is the Fellow Programme in Management, a doctoral-level research program in management studies offered by the Indian Institutes of Management.
-
E.
FMU
FMU is a public university in Florence, South Carolina, known for its liberal arts and professional programs.
- 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: FMEP Triple: [Pierrefonds Airport, ICAOcode, FMEP]
Generated description
FMEP is the ICAO airport code for Pierrefonds Airport on Réunion Island in the Indian Ocean.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FMEP Target entity description: FMEP is the ICAO airport code for Pierrefonds Airport on Réunion Island in the Indian Ocean.
-
A.
EFPM
EFPM is the Executive Fellow Programme in Management, a doctoral-level program designed for working professionals to pursue advanced research in management.
-
B.
MEF
MEF is Italy’s Ministry of Economy and Finance, the government department responsible for national economic policy, public finances, and the state budget.
-
C.
MPEA
MPEA is the commonly used abbreviation for the Metropolitan Pier and Exposition Authority, the public agency that owns and operates Chicago’s McCormick Place convention center and Navy Pier.
-
D.
FPM
FPM is the Fellow Programme in Management, a doctoral-level research program in management studies offered by the Indian Institutes of Management.
-
E.
FMU
FMU is a public university in Florence, South Carolina, known for its liberal arts and professional programs.
- 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_69c687e68e748190baceb9298f32d3ed |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c69f3db330819092503af4fb0649ea |
completed | March 27, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cb6f934481908a95d7424aa23414 |
completed | March 27, 2026, 6:24 p.m. |
| NEDg | Description generation | batch_69c6cd88f66c81909b364a816aeee8bf |
completed | March 27, 2026, 6:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6ce3a53cc8190a40d696a22ec65f4 |
completed | March 27, 2026, 6:36 p.m. |
Created at: March 27, 2026, 1:44 p.m.