Triple
T24579777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IFFHS World’s Best Referee |
E608214
|
entity |
| Predicate | hasGenderSpecificEdition |
P48671
|
FINISHED |
| Object | IFFHS World’s Best Woman Referee |
—
|
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: IFFHS World’s Best Woman Referee | Statement: [IFFHS World’s Best Referee, hasGenderSpecificEdition, IFFHS World’s Best Woman Referee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderSpecificEdition Context triple: [IFFHS World’s Best Referee, hasGenderSpecificEdition, IFFHS World’s Best Woman Referee]
-
A.
hasGenderVariant
chosen
Indicates that one entity is a gender-specific form or variant of another entity.
-
B.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
C.
includesBothGenders
Indicates that the referenced group, set, or category contains members of both male and female genders.
-
D.
hasGenderConvention
Indicates that there is an established or customary way of assigning or expressing gender within a given context, system, or culture.
-
E.
hasGenderFormat
Indicates that something is associated with or expressed in a particular gender-related format or representation.
- F. None of above.
Provenance (3 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_69e2c4cdab6c8190aae6e5d3de55c95e |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a97fde9c81909d8de91b6358a015 |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:29 a.m.