Triple
T2110046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Airlines Flight 175 |
E42480
|
entity |
| Predicate | aircraftModel |
P1524
|
FINISHED |
| Object | 767-222 |
—
|
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: 767-222 | Statement: [United Airlines Flight 175, aircraftModel, 767-222]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftModel Context triple: [United Airlines Flight 175, aircraftModel, 767-222]
-
A.
aircraftType
chosen
Indicates the specific model or category of aircraft associated with an entity or event.
-
B.
aircraftConfigurationProduced
Indicates that a specific aircraft configuration has been manufactured or produced.
-
C.
aircraftModelProduced
Indicates that a specific aircraft model has been manufactured or produced by a particular entity (such as a company or organization).
-
D.
usedOnAircraftName
Indicates that something is employed or applied on an aircraft identified by a specific name.
-
E.
planeName
Indicates that an entity has a specific name assigned to a plane (such as an aircraft or geometric plane).
- 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_69a8871040f08190aac2e2d0ab6b47ad |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abbae1bacc8190beffc9d0470e9190 |
completed | March 7, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_69abb7ba08948190a3c236bb53ee4257 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:43 p.m.