Triple
T36676304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2018 NHL All-Star Game |
E905548
|
entity |
| Predicate | numberOfGoaliesPerTeam |
P63966
|
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: [2018 NHL All-Star Game, numberOfGoaliesPerTeam, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGoaliesPerTeam Context triple: [2018 NHL All-Star Game, numberOfGoaliesPerTeam, 2]
-
A.
hasNumberOfGoaltenders
chosen
Indicates the specific count of goaltenders associated with a given team, roster, or game context.
-
B.
numberOfPlayersOnIcePerTeam
Indicates the count of players from a single team who are on the ice at the same time during play.
-
C.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
D.
hasNumberOfDefencemen
Indicates the relationship that specifies how many defencemen are associated with or assigned to a given entity.
-
E.
numberOfTeamsOnFieldPerSide
Indicates how many teams are present on the field for each opposing side 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_69f76e7011dc819082b324f18b756a1b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0e039481908a4a2666f76c5363 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:12 p.m.