Triple
T31133870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sony α7C |
E793584
|
entity |
| Predicate | contrastDetectionAFPoints |
P194709
|
FINISHED |
| Object | 425 |
—
|
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 | Statement: [Sony α7C, contrastDetectionAFPoints, 425]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contrastDetectionAFPoints Context triple: [Sony α7C, contrastDetectionAFPoints, 425]
-
A.
contrastAFPoints
Indicates a relationship where autofocus points are compared or differentiated to highlight their differences or opposing characteristics.
-
B.
contrastDetectionPoints
chosen
Indicates a relationship where specific points are identified or used for detecting contrast differences between elements or regions.
-
C.
contrastCapability
Indicates a relationship where one entity’s capabilities are compared or set in opposition to another’s, highlighting differences in what they can do or achieve.
-
D.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
E.
hasAnimalDetectionAF
Indicates that an entity possesses or is associated with an animal-detection autofocus capability.
- F. None of above.
Provenance (3 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_69f224d1701c819094f429798290e361 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd8ccbd4c88190b13aae0673b3c821 |
completed | May 8, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69fd8ae2227c819089546f5c3629799e |
completed | May 8, 2026, 7:04 a.m. |
Created at: April 29, 2026, 9:05 p.m.