Triple
T8121142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Force Z |
E189609
|
entity |
| Predicate | airSupportActual |
P6269
|
FINISHED |
| Object | insufficient and ineffective |
—
|
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: insufficient and ineffective | Statement: [Force Z, airSupportActual, insufficient and ineffective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airSupportActual Context triple: [Force Z, airSupportActual, insufficient and ineffective]
-
A.
airSupport
chosen
Indicates that one entity provides aerial assistance or backing to another, typically through aircraft-based protection, transport, or attack.
-
B.
supportsAircraft
Indicates that one entity is capable of accommodating, carrying, or enabling the operation of an aircraft.
-
C.
airframer
Indicates a relationship where a company designs, manufactures, or assembles aircraft as its primary aerospace activity.
-
D.
embarkedAircraft
Indicates that one entity boarded or got onto an aircraft as a passenger or occupant.
-
E.
appliesToAirport
Indicates that something is relevant, valid, or specifically intended for use at a particular airport.
- 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_69ca82bb74848190afb1f18640632c10 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4664fef881908b0dc7b158aca398 |
completed | March 31, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69cb368e7f4c81909aabd7716f0de79d |
completed | March 31, 2026, 2:50 a.m. |
Created at: March 30, 2026, 5:33 p.m.