Triple
T24580136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rugby League World Cup 9s |
E608224
|
entity |
| Predicate | numberOfParticipatingTeamsIn2019Women |
P99758
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Rugby League World Cup 9s, numberOfParticipatingTeamsIn2019Women, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfParticipatingTeamsIn2019Women Context triple: [Rugby League World Cup 9s, numberOfParticipatingTeamsIn2019Women, 4]
-
A.
womenTeamsCount
chosen
Indicates the number of teams composed of women associated with a given entity or context.
-
B.
hasWomenTeamInLeague
Indicates that an entity has a women’s team that participates in a specified league.
-
C.
featuresWomenTeams
Indicates that the subject includes, presents, or involves teams composed of women.
-
D.
womenTeam
Indicates that the team is composed of women or is designated as a women’s team.
-
E.
includesWomen'sLeagues
Indicates that the subject entity encompasses, offers, or is associated with one or more leagues specifically organized for women.
- 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_69e2c4ce89248190ad99e18f0638dfbb |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a97fde9c81909d8de91b6358a015 |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:29 a.m.