Triple
T27576819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt Helders |
E699481
|
entity |
| Predicate | hasClothingLine |
P175632
|
FINISHED |
| Object | Denim and Leather |
—
|
NE NERFINISHED |
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: Denim and Leather | Statement: [Matt Helders, hasClothingLine, Denim and Leather]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasClothingLine Context triple: [Matt Helders, hasClothingLine, Denim and Leather]
-
A.
hasGarment
Indicates that one entity possesses, wears, or is associated with a particular garment.
-
B.
hasFragranceLine
Indicates that one entity (typically a brand or company) offers or is associated with a particular line or collection of fragrances.
-
C.
hasClothingSource
Indicates that an entity’s clothing originates from, is supplied by, or is obtained through a specified source.
-
D.
hasFashionArm
Indicates that an entity possesses or is equipped with a specific fashion-related arm or arm accessory.
-
E.
showsClothing
Indicates that one entity visually presents or displays an item of clothing associated with 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_69ef6a4cb8b881909b3a8d630fd89df2 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d26ceb08819091c71c001e954936 |
completed | May 3, 2026, 4:43 a.m. |
| PDg | Predicate description generation | batch_69f6d6a482fc8190b526291cd99b8696 |
completed | May 3, 2026, 5:01 a.m. |
Created at: April 27, 2026, 2:01 p.m.