Triple
T3428611
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TAG Heuer Formula 1 |
E72283
|
entity |
| Predicate | hasGenderVariant |
P48671
|
FINISHED |
| Object | men's models |
—
|
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: men's models | Statement: [TAG Heuer Formula 1, hasGenderVariant, men's models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderVariant Context triple: [TAG Heuer Formula 1, hasGenderVariant, men's models]
-
A.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
B.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
C.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
D.
hasGenderInSomeTraditions
Indicates that, in at least some cultural, religious, or historical traditions, the subject is regarded as having a specific gender.
-
E.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
- 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_69ad85ae14308190bcbc25cfa0246c0b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb983f4608190abcc27aa7b926deb |
completed | March 8, 2026, 6:01 p.m. |
| PD | Predicate disambiguation | batch_69adadfea024819094b41a13bc004bda |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb00f4f8c81908f88daf71f6a9c29 |
completed | March 8, 2026, 5:21 p.m. |
Created at: March 8, 2026, 3:15 p.m.