Triple
T33645523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GG canvas |
E861949
|
entity |
| Predicate | commonlyTrimmedWith |
P72667
|
FINISHED |
| Object | leather |
—
|
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: leather | Statement: [GG canvas, commonlyTrimmedWith, leather]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonlyTrimmedWith Context triple: [GG canvas, commonlyTrimmedWith, leather]
-
A.
notableTrim
Indicates that an entity has a particularly significant or distinguished trim level or decorative variant compared to standard versions.
-
B.
relatedTrim
chosen
Indicates that one entity is associated with another as a corresponding or compatible trim level or variant.
-
C.
commonCut
Indicates that two or more entities share at least one identical segment or portion that has been cut or divided in the same way.
-
D.
isTrimOf
Indicates that one entity is a version of another with leading and/or trailing whitespace characters removed.
-
E.
commonIn
Indicates that something frequently occurs, appears, or is found within a specified context, group, or environment.
- 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_69f3498280c48190bcc3494017d14234 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe91383a1c81909266e40c3c3ede6c |
completed | May 9, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69fe8fde094081908f0f121664fbb5c7 |
completed | May 9, 2026, 1:37 a.m. |
Created at: May 1, 2026, 1:42 a.m.