Triple
T35475367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kendrew Barracks |
E1025316
|
entity |
| Predicate | hasAirfieldOrigin |
P2882
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Kendrew Barracks, hasAirfieldOrigin, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAirfieldOrigin Context triple: [Kendrew Barracks, hasAirfieldOrigin, yes]
-
A.
hasOriginAirport
Indicates that something, typically a flight or journey, departs from or is associated with a specific origin airport.
-
B.
hasRunwayOrigin
Indicates that something originates from, starts at, or is derived from a particular runway.
-
C.
hasTypicalOriginAirport
Indicates that an entity, such as a flight route or airline service, is commonly or usually associated with a particular origin airport from which it typically departs.
-
D.
containsAirfield
chosen
Indicates that a location or area includes at least one airfield within its boundaries.
-
E.
hasOriginAirportCity
Indicates that an entity (such as a flight or trip) departs from or is associated with a specific origin airport located in a given city.
- F. None of above.
Provenance (3 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_69f76dfadba0819083456aadcd6864ea |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:04 p.m.