Triple
T5355387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexander Mogilny |
E102676
|
entity |
| Predicate | seasonGoals |
P9098
|
FINISHED |
| Object | 76 goals in the 1992–93 NHL season |
—
|
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: 76 goals in the 1992–93 NHL season | Statement: [Alexander Mogilny, seasonGoals, 76 goals in the 1992–93 NHL season]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seasonGoals Context triple: [Alexander Mogilny, seasonGoals, 76 goals in the 1992–93 NHL season]
-
A.
numberOfGoals
chosen
Indicates the total count of goals scored or achieved by an entity in a given context.
-
B.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
-
C.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
D.
leagueGoalsAgainst
Indicates the number of goals a team has conceded in league competition against its opponents.
-
E.
worldCupGoals
Indicates the number of goals an entity scored in World Cup matches.
- 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_69bd43d8f7248190b64c140734b5c9a8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd862dbb008190aef653acddafd38b |
completed | March 20, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69bd845c6f108190832a8d14b356368a |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:01 p.m.