Triple
T25889857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Essendon (AFLW) |
E652298
|
entity |
| Predicate | isWomenCounterpartOf |
P158000
|
FINISHED |
| Object | Essendon Football Club men’s team |
—
|
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: Essendon Football Club men’s team | Statement: [Essendon (AFLW), isWomenCounterpartOf, Essendon Football Club men’s team]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isWomenCounterpartOf Context triple: [Essendon (AFLW), isWomenCounterpartOf, Essendon Football Club men’s team]
-
A.
femaleCounterpartOf
chosen
Indicates that one entity is the female equivalent or corresponding counterpart of another entity within a given role, relationship, or category.
-
B.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
C.
femalePartner
Indicates that one entity is the female partner in a romantic or marital relationship with the other entity.
-
D.
womenStatus
Indicates the social, legal, economic, or cultural position or condition assigned to women within a given context or system.
-
E.
femaleMember
Indicates that one entity is a member of a group or organization and is identified as female.
- 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_69e7ab3b92cc81908febd90317862647 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6037de5c88190a493c1fc3ccc81b7 |
completed | May 2, 2026, 2 p.m. |
| PD | Predicate disambiguation | batch_69f4a0fed15881909b789251fe5d8d45 |
completed | May 1, 2026, 12:47 p.m. |
Created at: April 22, 2026, 8:19 a.m.