Triple
T11031033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RSA |
E260755
|
entity |
| Predicate | genderOfTeamRepresented |
P35850
|
FINISHED |
| Object | men's team |
—
|
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 team | Statement: [RSA, genderOfTeamRepresented, men's team]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfTeamRepresented Context triple: [RSA, genderOfTeamRepresented, men's team]
-
A.
hasGenderedTeam
Indicates that a team is organized or classified according to the gender of its members.
-
B.
hasGenderedTeams
chosen
Indicates that the entity organizes or participates in teams that are separated or defined based on gender.
-
C.
womenTeam
Indicates that the team is composed of women or is designated as a women’s team.
-
D.
parentClubGender
Indicates the gender category (e.g., men’s, women’s, mixed) associated with the parent club in a club–team or club–division relationship.
-
E.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
- 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797d3bba08190b5134e520225baca |
completed | April 9, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69d7440087ac8190aef2e6f6b13b2635 |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:25 p.m.