Triple
T33834820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LFBP |
E867200
|
entity |
| Predicate | associatedAirportDepartment |
P206691
|
FINISHED |
| Object | Pyrénées-Atlantiques |
E123114
|
NE FINISHED |
How this triple was built (2 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: Pyrénées-Atlantiques | Statement: [LFBP, associatedAirportDepartment, Pyrénées-Atlantiques]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedAirportDepartment Context triple: [LFBP, associatedAirportDepartment, Pyrénées-Atlantiques]
-
A.
belongsToAirport
Indicates that one entity is part of, associated with, or under the jurisdiction of a specific airport.
-
B.
belongsToAirportSystem
Indicates that an airport is a member or component of a specific airport system or network.
-
C.
partOfAirportType
Indicates that something is a component or subdivision belonging to a specific type or category of airport.
-
D.
associatedAirport
Indicates a relationship where an entity is linked or connected to a specific airport, typically as its relevant or corresponding airport.
-
E.
associatedHubAirport
Indicates that one entity serves as a primary or hub airport functionally linked to the other entity.
- 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_69f34992ad40819087760ed939bd2a7a |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037e0953908190b2930b3c06a40129 |
completed | May 12, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a36822ef8e88190954ed0d51c1f84a2 |
completed | June 20, 2026, 12:06 p.m. |
| PD | Predicate disambiguation | batch_6a0379f6c3308190b954f7810214ceed |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037e07fe4481909ca21eae7a941ee7 |
completed | May 12, 2026, 7:22 p.m. |
Created at: May 1, 2026, 1:47 a.m.