Triple
T9910515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian football league system |
E185128
|
entity |
| Predicate | hasWomenParallelSystem |
P1613
|
FINISHED |
| Object | Italian women's football league system |
—
|
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: Italian women's football league system | Statement: [Italian football league system, hasWomenParallelSystem, Italian women's football league system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWomenParallelSystem Context triple: [Italian football league system, hasWomenParallelSystem, Italian women's football league system]
-
A.
hasWomenTeamPlan
Indicates that an entity offers or is associated with a specific plan or program designed for women’s teams.
-
B.
hasWomenOrganization
Indicates that an entity is associated with, contains, or is part of an organization specifically for women.
-
C.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
-
D.
hasFemaleEquivalent
chosen
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
E.
hadWomenOrganization
Indicates that an entity was associated with or involved in an organization focused on women or women’s issues.
- 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_69ca8296165881908ca4750701af1f29 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb512a26881908eb72a21ffb1efef |
completed | April 2, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69cd1d8c584081908b73de75eb18e438 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:41 p.m.