Triple
T5870451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steel City derby |
E130501
|
entity |
| Predicate | hasWomenTeamsVariant |
P13104
|
FINISHED |
| Object | Sheffield United Women vs Sheffield Wednesday Ladies (occasional) |
—
|
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: Sheffield United Women vs Sheffield Wednesday Ladies (occasional) | Statement: [Steel City derby, hasWomenTeamsVariant, Sheffield United Women vs Sheffield Wednesday Ladies (occasional)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWomenTeamsVariant Context triple: [Steel City derby, hasWomenTeamsVariant, Sheffield United Women vs Sheffield Wednesday Ladies (occasional)]
-
A.
hasGenderedTeams
Indicates that the entity organizes or participates in teams that are separated or defined based on gender.
-
B.
hasWomenTeamPlan
Indicates that an entity offers or is associated with a specific plan or program designed for women’s teams.
-
C.
hasMenTeam
Indicates that an entity possesses, is associated with, or fields a men’s team.
-
D.
hasGenderedTeam
Indicates that a team is organized or classified according to the gender of its members.
-
E.
womenTeam
chosen
Indicates that the team is composed of women or is designated as a women’s team.
- 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_69c0085047dc8190af24e311edad3c07 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ffaef081909faaa7f420a3b9b7 |
completed | March 22, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69c03347e51c81909053bcf34e3b88ab |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:56 p.m.