Triple
T14373888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Characters Edition Andy Warhol |
E356423
|
entity |
| Predicate | hasClip |
P113789
|
FINISHED |
| Object | metal clip |
—
|
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: metal clip | Statement: [Great Characters Edition Andy Warhol, hasClip, metal clip]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasClip Context triple: [Great Characters Edition Andy Warhol, hasClip, metal clip]
-
A.
hasCut
Indicates that one entity has made or possesses a cut in, on, or through another entity.
-
B.
hasCue
Indicates that one entity provides, contains, or is associated with a signal or prompt that can guide or trigger another entity’s behavior or response.
-
C.
hasCP
Indicates that an entity possesses, is associated with, or is characterized by a specific CP (such as a control point, contact person, or configuration parameter), depending on the domain context.
-
D.
supportsMultiClipEditing
Indicates that the subject provides functionality to edit multiple clips simultaneously within the same editing context.
-
E.
hasTypicalCut
Indicates that one entity is characterized by or associated with a standard or typical type of cut of another entity.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de9007184c8190aebb003cb6548cc8 |
completed | April 14, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69de2a9cb3e081909f6b33fdd939bb9e |
completed | April 14, 2026, 11:53 a.m. |
| PDg | Predicate description generation | batch_69de2e07d1f88190bdcd20967e484718 |
completed | April 14, 2026, 12:07 p.m. |
Created at: April 10, 2026, 1:15 a.m.