Triple
T33819945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blériot Aéronautique |
E866799
|
entity |
| Predicate | hasAircraftModel |
P67179
|
FINISHED |
| Object | Blériot XI |
—
|
NE NERFINISHED |
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: Blériot XI | Statement: [Blériot Aéronautique, hasAircraftModel, Blériot XI]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAircraftModel Context triple: [Blériot Aéronautique, hasAircraftModel, Blériot XI]
-
A.
associatedWithAircraftModel
chosen
Indicates that something has a specified relationship or connection to a particular aircraft model.
-
B.
hasBasedAircraft
Indicates that an aircraft is regularly stationed or primarily based at a particular location or facility.
-
C.
hadBasedAircraftType
Indicates that a particular type of aircraft was stationed at or operated from a specific base or location.
-
D.
testedAircraftOfManufacturer
Indicates that an entity (typically a person or organization) has performed testing on an aircraft produced by a specified manufacturer.
-
E.
specificCarrierAircraftVariant
Indicates that one aircraft variant is a specific version designed or adapted for carrier-based operations of another, more general aircraft variant.
- 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_69f349911a8c81908478662194b23d8c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7051ad6e4819095e82bbd64761803 |
completed | May 3, 2026, 8:19 a.m. |
| PD | Predicate disambiguation | batch_69f700fe24e08190998e2c96fbaaad38 |
completed | May 3, 2026, 8:02 a.m. |
Created at: May 1, 2026, 1:46 a.m.