Triple
T5454330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VistaVision |
E122441
|
entity |
| Predicate | negativeType |
P64126
|
FINISHED |
| Object | spherical lenses |
—
|
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: spherical lenses | Statement: [VistaVision, negativeType, spherical lenses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: negativeType Context triple: [VistaVision, negativeType, spherical lenses]
-
A.
negativeFormulation
Indicates that the associated statement, condition, or requirement is expressed in a negated or prohibitive form rather than an affirmative one.
-
B.
negates
Indicates that one entity denies, contradicts, or renders false the assertion, state, or effect expressed by another.
-
C.
negativeAcknowledgmentCharacter
Indicates that one character responds with disagreement, refusal, or denial to another character’s statement, request, or action.
-
D.
negativeAnswerYear
Indicates that an answer, decision, or response given in a particular year was negative (e.g., a refusal, denial, or rejection) associated with the subject.
-
E.
neutralized
Indicates that one entity has rendered another entity ineffective, harmless, or no longer able to exert its intended effect.
- 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_69bd46424248819085282ddf50a565f3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927c946c8190aef40679199fede3 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a0d96c8190bd1299edbf764bbb |
completed | March 20, 2026, 6:27 p.m. |
| PDg | Predicate description generation | batch_69bd927b0b4c81909d5e0f594822e3f9 |
completed | March 20, 2026, 6:31 p.m. |
Created at: March 20, 2026, 2:08 p.m.