Triple
T30644073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sony α7 III |
E780071
|
entity |
| Predicate | contrastAFPoints |
P194313
|
FINISHED |
| Object | 425 contrast-detection AF points |
—
|
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: 425 contrast-detection AF points | Statement: [Sony α7 III, contrastAFPoints, 425 contrast-detection AF points]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contrastAFPoints Context triple: [Sony α7 III, contrastAFPoints, 425 contrast-detection AF points]
-
A.
contrastUse
Indicates that one entity is used in opposition or distinction to another to highlight differences between them.
-
B.
contrastCharacteristic
Indicates that two entities are being compared by highlighting opposing or significantly different characteristics between them.
-
C.
contrastExplanation
Indicates an explanation that highlights differences between two or more entities, ideas, or situations by contrasting them.
-
D.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
E.
contrastGoal
Indicates a relationship where one goal is defined in opposition to, or as a contrasting alternative to, another goal.
- 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_69f224a50ebc81909b961a94c7f66b12 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd6a1c1c4881908090053bc359b181 |
completed | May 8, 2026, 4:44 a.m. |
| PD | Predicate disambiguation | batch_69fd696f24d8819091033afacbdaadc5 |
completed | May 8, 2026, 4:41 a.m. |
| PDg | Predicate description generation | batch_69fd6a1a38f081908c573aee4696de4f |
completed | May 8, 2026, 4:44 a.m. |
Created at: April 29, 2026, 8:29 p.m.