Triple
T6490930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marcel Dionne |
E148033
|
entity |
| Predicate | rankAllTimeGoals |
P71035
|
FINISHED |
| Object | top 10 in NHL history |
—
|
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: top 10 in NHL history | Statement: [Marcel Dionne, rankAllTimeGoals, top 10 in NHL history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankAllTimeGoals Context triple: [Marcel Dionne, rankAllTimeGoals, top 10 in NHL history]
-
A.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
B.
fifaCenturyGoals
Indicates that a player has scored at least 100 goals in official FIFA-recognized international matches.
-
C.
worldCupGoals
Indicates the number of goals an entity scored in World Cup matches.
-
D.
topScorer
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
-
E.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
- 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_69c009088f3081909cd467b05919de30 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a9a8d8481908d88e5c9f0c773f7 |
completed | March 22, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69c06740bebc81909d9d6956baa2bcb9 |
completed | March 22, 2026, 10:03 p.m. |
| PDg | Predicate description generation | batch_69c067f1ef148190bc0355abe83f7e16 |
completed | March 22, 2026, 10:06 p.m. |
Created at: March 22, 2026, 4:53 p.m.