Triple
T13316368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arthur’s Seat coffins |
E317197
|
entity |
| Predicate | hasGenderRepresentation |
P109486
|
FINISHED |
| Object | mostly male figurines |
—
|
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: mostly male figurines | Statement: [Arthur’s Seat coffins, hasGenderRepresentation, mostly male figurines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderRepresentation Context triple: [Arthur’s Seat coffins, hasGenderRepresentation, mostly male figurines]
-
A.
hasGenderNeutrality
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
-
B.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
C.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
D.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
-
E.
hasGenderRole
Indicates that an entity is associated with, or expected to perform, a particular socially defined gender-based role or set of behaviors.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6babd88190a5d529df9584b9a4 |
completed | April 11, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69d99cf7f9c48190a6a4f452b4a2aefa |
completed | April 11, 2026, 12:59 a.m. |
Created at: April 9, 2026, 9:29 p.m.