Triple
T4497159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cam Neely |
E100724
|
entity |
| Predicate | scored50GoalsIn |
P31006
|
FINISHED |
| Object | 44 games (1993–94 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: 44 games (1993–94 season) | Statement: [Cam Neely, scored50GoalsIn, 44 games (1993–94 season)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scored50GoalsIn Context triple: [Cam Neely, scored50GoalsIn, 44 games (1993–94 season)]
-
A.
consecutive50GoalSeasons
Indicates that an entity has achieved 50 or more goals in each of multiple consecutive seasons.
-
B.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
C.
NHL50GoalSeasons
chosen
Indicates that a player has achieved one or more NHL seasons in which they scored at least 50 goals.
-
D.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
E.
topScorer
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
- 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_69bd43cdf15081909a4fa2585ff63b3e |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56bf3ff48190b3aae0136d7fce45 |
completed | March 20, 2026, 2:16 p.m. |
| PD | Predicate disambiguation | batch_69bd521671688190bc655d25fa77eba2 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1 p.m.