Triple
T10253540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentina national rugby union team |
E240403
|
entity |
| Predicate | hasProducedNotablePlayers |
P9730
|
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: [Argentina national rugby union team, hasProducedNotablePlayers, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProducedNotablePlayers Context triple: [Argentina national rugby union team, hasProducedNotablePlayers, yes]
-
A.
hasNotablePlayer
chosen
Indicates that an entity (such as a team or club) is associated with a player who is particularly distinguished, famous, or significant in its context.
-
B.
hasProducedPlayersFor
Indicates that one entity has been the source or origin of players who go on to be associated with, or participate for, another entity.
-
C.
notableAthlete
Indicates that the subject is a well-known or distinguished athlete associated with the object (such as a sport, team, or organization).
-
D.
hasNotableAlumniType
Indicates that an entity has notable alumni belonging to a specified category or type.
-
E.
notablePlayersCoached
Indicates that a coach has trained or mentored specific players who are considered notable or distinguished in their field.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d328272c8190a3548d7f7f38cfc4 |
completed | April 7, 2026, 9:49 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ebd6c88190a1f3f4a72a99d6fe |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:29 a.m.