Triple
T24580135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rugby League World Cup 9s |
E608224
|
entity |
| Predicate | numberOfParticipatingTeamsIn2019Men |
P6986
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [Rugby League World Cup 9s, numberOfParticipatingTeamsIn2019Men, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfParticipatingTeamsIn2019Men Context triple: [Rugby League World Cup 9s, numberOfParticipatingTeamsIn2019Men, 12]
-
A.
hasNumberOfTeams
chosen
Indicates the quantity of teams associated with or contained by a given entity.
-
B.
numberOfTeamsVariesByYear
Indicates that the number of teams involved changes depending on the specific year.
-
C.
hasMenTeam
Indicates that an entity possesses, is associated with, or fields a men’s team.
-
D.
maximumTeamsPerNationPerGender
Indicates the upper limit on how many teams from a single nation are allowed to participate for each gender category.
-
E.
womenTeamsCount
Indicates the number of teams composed of women associated with a given entity 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_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.