Triple
T2667770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Todd-AO |
E55677
|
entity |
| Predicate | cameraLensType |
P41556
|
FINISHED |
| Object | wide-angle 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: wide-angle lenses | Statement: [Todd-AO, cameraLensType, wide-angle lenses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cameraLensType Context triple: [Todd-AO, cameraLensType, wide-angle lenses]
-
A.
laterLensType
Indicates that one lens type occurs or is used at a later time than another lens type in a temporal sequence.
-
B.
objectiveLensDiameter
Indicates the diameter of the objective lens used in an optical device, such as a camera or telescope.
-
C.
cameraStyle
Indicates the characteristic visual approach or technique used by a camera in capturing or presenting imagery.
-
D.
usesLensMount
Indicates that one device or component is designed to accept, attach to, or operate with a specific type of lens mount.
-
E.
cameraBranding
Indicates that one entity serves as the brand or branding designation associated with a camera or camera product.
- 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_69ab49e54de48190be708cd1cf8be073 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd98a4ee88190aa7ef914e316ba31 |
completed | March 7, 2026, 7:53 a.m. |
| PD | Predicate disambiguation | batch_69abd8190ad481908f3e14ac84d0940a |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8f98c348190a68064c565589459 |
completed | March 7, 2026, 7:51 a.m. |
Created at: March 6, 2026, 9:54 p.m.