Triple
T12107207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MGM Camera 65 |
E288332
|
entity |
| Predicate | negativeWidth |
P103319
|
FINISHED |
| Object | 65 mm |
—
|
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: 65 mm | Statement: [MGM Camera 65, negativeWidth, 65 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: negativeWidth Context triple: [MGM Camera 65, negativeWidth, 65 mm]
-
A.
negativeAspectRatio
Indicates that the relationship or configuration between entities involves an aspect ratio value that is negative or otherwise invalid in sign.
-
B.
negativeType
Indicates that one entity is classified as a negative, undesirable, or disfavored type in relation to another or within a given context.
-
C.
negativeMarking
Indicates that an entity assigns or receives a penalty, deduction, or unfavorable score in response to a particular action, performance, or condition.
-
D.
negativeFormulation
Indicates that the associated statement, condition, or requirement is expressed in a negated or prohibitive form rather than an affirmative one.
-
E.
negativePhaseAssociatedWith
Indicates an association between something and a negative or unfavorable phase, state, or stage in a process or condition.
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9164ada5081908676bd9e5947268a |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150497408190921334d21503375a |
completed | April 10, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69d916481a008190ae66677b9e6dd961 |
completed | April 10, 2026, 3:24 p.m. |
Created at: April 8, 2026, 9:49 p.m.