Triple
T2928505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1992–93 NHL season |
E78901
|
entity |
| Predicate | totalGoalsRecord |
P43998
|
FINISHED |
| Object | highest total goals in a single NHL season era |
—
|
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: highest total goals in a single NHL season era | Statement: [1992–93 NHL season, totalGoalsRecord, highest total goals in a single NHL season era]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalGoalsRecord Context triple: [1992–93 NHL season, totalGoalsRecord, highest total goals in a single NHL season era]
-
A.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
B.
worldCupGoals
Indicates the number of goals an entity scored in World Cup matches.
-
C.
worldCupRecord
Indicates a relationship that specifies an entity’s performance statistics or achievements in FIFA World Cup competitions.
-
D.
mostOverallWinsRecord
Indicates that the subject holds the record for having the greatest total number of wins compared to all others in the relevant context.
-
E.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
- F. None of above. chosen
Provenance (4 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_69ad8b0d40b481908bc2a5fa2e73c3fb |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad97ff0ddc8190acba9863bbe4f54b |
completed | March 8, 2026, 3:38 p.m. |
| PD | Predicate disambiguation | batch_69ad9606e8348190bb19df33a2709674 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f520208190a4dc43372004555f |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 2:55 p.m.