Triple
T650258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Air Force roundel |
E11330
|
entity |
| Predicate | locationOnAircraft |
P17750
|
FINISHED |
| Object | fuselage sides |
—
|
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: fuselage sides | Statement: [French Air Force roundel, locationOnAircraft, fuselage sides]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locationOnAircraft Context triple: [French Air Force roundel, locationOnAircraft, fuselage sides]
-
A.
aircraftFacility
Indicates that a facility is designed, equipped, or used to support the operation, maintenance, or accommodation of aircraft.
-
B.
seatOftenLocatedIn
Indicates that one type of seat is commonly or typically found within a particular location or setting.
-
C.
usedOnAircraftName
Indicates that something is employed or applied on an aircraft identified by a specific name.
-
D.
partOfAirportLocatedIn
Indicates that a specific part or component of an airport is geographically or administratively located within a particular area or region.
-
E.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
- F. None of above. chosen
Provenance (4 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f31e70c81909a2ac1d939f7ec07 |
completed | March 1, 2026, 8:18 p.m. |
| PD | Predicate disambiguation | batch_69a49d0eade081909c47e85ed55f808d |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49df0de3c81909721eb391ec94031 |
completed | March 1, 2026, 8:13 p.m. |
Created at: March 1, 2026, 7:36 p.m.