Triple
T7594737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NHL First All-Star Team |
E179828
|
entity |
| Predicate | numberOfDefencemen |
P63967
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [NHL First All-Star Team, numberOfDefencemen, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDefencemen Context triple: [NHL First All-Star Team, numberOfDefencemen, 2]
-
A.
hasNumberOfDefencemen
chosen
Indicates the relationship that specifies how many defencemen are associated with or assigned to a given entity.
-
B.
hasNumberOfGoaltenders
Indicates the specific count of goaltenders associated with a given team, roster, or game context.
-
C.
numberOfPlayersOnIcePerTeam
Indicates the count of players from a single team who are on the ice at the same time during play.
-
D.
bestDefenceman
Indicates that the subject is recognized as the top-performing or most outstanding defenceman among a specified group or within a particular context.
-
E.
numberOfBrothersInNHL
Indicates the count of a person's brothers who have played in the NHL.
- 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_69c69f3487ec8190bf7acdf2dd91e6d6 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9bbcd8081909a229d7faa2ffdc8 |
completed | March 27, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e2e42c8190afc802c4796c9cc2 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:53 p.m.