Triple
T23442647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | H. Jon Benjamin as Bob Belcher |
E565443
|
entity |
| Predicate | characterSignatureClothing |
P152299
|
FINISHED |
| Object | whiteTShirt |
—
|
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: whiteTShirt | Statement: [H. Jon Benjamin as Bob Belcher, characterSignatureClothing, whiteTShirt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterSignatureClothing Context triple: [H. Jon Benjamin as Bob Belcher, characterSignatureClothing, whiteTShirt]
-
A.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
-
B.
clothingSymbolism
Indicates how clothing or attire conveys symbolic meaning, such as status, identity, emotion, or cultural significance, within a given context.
-
C.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
-
D.
personHasNotableStyle
Indicates that a person is recognized for having a distinctive or noteworthy style.
-
E.
shirtSymbol
Indicates that one entity bears, displays, or is marked with a particular symbol on a shirt.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64654e88190b530958b27b32412 |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
| PDg | Predicate description generation | batch_69f07cbbd7488190ab3c8ae7d0fb68bf |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 17, 2026, 5:51 p.m.