Triple
T14373885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Characters Edition Andy Warhol |
E356423
|
entity |
| Predicate | hasCapType |
P113787
|
FINISHED |
| Object | screw-on cap |
—
|
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: screw-on cap | Statement: [Great Characters Edition Andy Warhol, hasCapType, screw-on cap]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCapType Context triple: [Great Characters Edition Andy Warhol, hasCapType, screw-on cap]
-
A.
hasCapacityType
Indicates that an entity possesses a specific kind or classification of capacity or capability.
-
B.
hasCapitalType
Indicates that a specified location’s capital is of a particular type (e.g., political, administrative, or economic capital).
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
capabilityType
Indicates the type or category of capability that an entity possesses or is associated with.
-
E.
hasCabType
Indicates that an entity is associated with or characterized by a specific type or category of cab.
- 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.