Triple
T18424628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing–Saab |
E442103
|
entity |
| Predicate | aircraftNameOrigin |
P68741
|
FINISHED |
| Object | named in honor of the Tuskegee Airmen "Red Tails" |
—
|
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: named in honor of the Tuskegee Airmen "Red Tails" | Statement: [Boeing–Saab, aircraftNameOrigin, named in honor of the Tuskegee Airmen "Red Tails"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftNameOrigin Context triple: [Boeing–Saab, aircraftNameOrigin, named in honor of the Tuskegee Airmen "Red Tails"]
-
A.
aircraftNicknameOrigin
chosen
Indicates the source or reason from which an aircraft’s nickname is derived.
-
B.
aircraftNicknamedFor
Indicates that an aircraft is commonly known or referred to by a particular nickname derived from or inspired by something else.
-
C.
usedAircraftOrigin
Indicates the place or source from which a used aircraft was originally obtained or came.
-
D.
firstAircraftName
Indicates the name assigned to the first aircraft associated with a given entity or context.
-
E.
originOfFlight
Indicates that one location serves as the starting point or departure place for a particular flight.
- 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51b1149788190ad7c453547a6e07c |
completed | April 19, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69e469bf7f74819096a01173493412c2 |
completed | April 19, 2026, 5:35 a.m. |
Created at: April 10, 2026, 10:47 a.m.