Triple
T7594736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NHL First All-Star Team |
E179828
|
entity |
| Predicate | numberOfGoaltenders |
P63966
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [NHL First All-Star Team, numberOfGoaltenders, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGoaltenders Context triple: [NHL First All-Star Team, numberOfGoaltenders, 1]
-
A.
hasNumberOfGoaltenders
chosen
Indicates the specific count of goaltenders associated with a given team, roster, or game context.
-
B.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
C.
goaltender
Indicates a relationship where an entity serves as the goalkeeper or primary defender of the goal for a team in a game or sport.
-
D.
hasNumberOfDefencemen
Indicates the relationship that specifies how many defencemen are associated with or assigned to a given entity.
-
E.
numberOfPlayersOnIcePerTeam
Indicates the count of players from a single team who are on the ice at the same time during play.
- 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.