Triple
T24936596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | P-80 Shooting Star |
E623327
|
entity |
| Predicate | armamentFixedCount |
P157260
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [P-80 Shooting Star, armamentFixedCount, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: armamentFixedCount Context triple: [P-80 Shooting Star, armamentFixedCount, 6]
-
A.
armamentCount
Indicates the number of weapons or armaments associated with an entity.
-
B.
armamentCapacity
Indicates the maximum quantity or type of weapons or munitions that something is designed or allowed to carry.
-
C.
ammunitionCapacity
Indicates the maximum amount of ammunition that something (typically a weapon or container) is designed to hold at one time.
-
D.
armamentFlexible
Indicates that an entity’s armament or weapon configuration can be adjusted, reconfigured, or adapted to different setups or roles.
-
E.
armamentConfiguration
Indicates how weapons or armaments are arranged, equipped, or configured on an entity in a given context.
- 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_69e2fac6b5a48190a1c38857f00915a9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f423d5a0f48190b4df532471cd37d1 |
completed | May 1, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69f4210130d08190ae30b7943f7a0bbc |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f423637bec8190a1701421ac86a3b7 |
completed | May 1, 2026, 3:52 a.m. |
Created at: April 18, 2026, 5:30 a.m.