Triple
T25178494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gold Coast Suns (AFLW) |
E630517
|
entity |
| Predicate | femaleCounterpartOf |
P158000
|
FINISHED |
| Object | Gold Coast Suns |
—
|
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: Gold Coast Suns | Statement: [Gold Coast Suns (AFLW), femaleCounterpartOf, Gold Coast Suns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleCounterpartOf Context triple: [Gold Coast Suns (AFLW), femaleCounterpartOf, Gold Coast Suns]
-
A.
femaleSubject
Indicates that the subject in the relationship or action is female.
-
B.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
C.
femaleMember
Indicates that one entity is a member of a group or organization and is identified as female.
-
D.
femalePartner
Indicates that one entity is the female partner in a romantic or marital relationship with the other entity.
-
E.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
- 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_69e75a88fdf081908e47ae6e195c14e1 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f46dc2ac088190a629119847b6097e |
completed | May 1, 2026, 9:09 a.m. |
| PD | Predicate disambiguation | batch_69f44d8043b081908bbffd7f044b4f26 |
completed | May 1, 2026, 6:51 a.m. |
| PDg | Predicate description generation | batch_69f45300bd488190bb1d4160f5534ef6 |
completed | May 1, 2026, 7:15 a.m. |
Created at: April 21, 2026, 12:35 p.m.