Triple
T30644151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canon EOS M6 Mark II |
E780072
|
entity |
| Predicate | videoCropIn4K |
P172182
|
FINISHED |
| Object | no significant crop (uses full sensor width) |
—
|
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: no significant crop (uses full sensor width) | Statement: [Canon EOS M6 Mark II, videoCropIn4K, no significant crop (uses full sensor width)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: videoCropIn4K Context triple: [Canon EOS M6 Mark II, videoCropIn4K, no significant crop (uses full sensor width)]
-
A.
foalCrop
Indicates a relationship where a foal is associated with or produced from a particular crop or breeding outcome.
-
B.
videoChip
Indicates a relationship where an entity functions as or contains a video-processing chip or component used for handling video signals or graphics.
-
C.
usesCrop
Indicates that one entity employs or applies a particular crop for a specific purpose or function.
-
D.
isCrop
Indicates that an entity functions as or is classified as an agricultural crop.
-
E.
cropMilk
Indicates that one entity extracts or expresses milk from another entity, typically by squeezing or manipulating it.
- 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_69f6a9603b208190b3533ea2b441514c |
completed | May 3, 2026, 1:48 a.m. |
| PD | Predicate disambiguation | batch_69f6a7548eb48190a69b60a3c6ad53b9 |
completed | May 3, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f6a915ead881909463ae46419c343e |
completed | May 3, 2026, 1:47 a.m. |
Created at: April 29, 2026, 8:29 p.m.