Triple
T13093049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rugby League Championship |
E310510
|
entity |
| Predicate | hasFrenchClubs |
P103892
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Rugby League Championship, hasFrenchClubs, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrenchClubs Context triple: [Rugby League Championship, hasFrenchClubs, yes]
-
A.
isOnlyFrenchClubToWin
Indicates that the subject is the sole French club to have achieved a particular victory or title, with no other French club having done so.
-
B.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
C.
hasSportsClubs
chosen
Indicates that an entity possesses, hosts, or is associated with one or more sports clubs.
-
D.
FrenchLeagueTitlesWith
Indicates a relationship where two entities are associated through having won French football league titles together (e.g., a club and a player sharing those titles).
-
E.
hasWonTopFlightLeagueInFrance
Indicates that the subject has won the championship title of the highest professional football league in France at least once.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d9813acbac8190b2fe5e07287457cf |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d9803f6c508190bfadfbc2d00c2c64 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:03 p.m.