Triple
T1600424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dwayne De Rosario |
E34377
|
entity |
| Predicate | sportNumberOfGoals |
P9098
|
FINISHED |
| Object | top scorer for Canada at time of retirement |
—
|
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 scorer for Canada at time of retirement | Statement: [Dwayne De Rosario, sportNumberOfGoals, top scorer for Canada at time of retirement]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sportNumberOfGoals Context triple: [Dwayne De Rosario, sportNumberOfGoals, top scorer for Canada at time of retirement]
-
A.
numberOfGoals
chosen
Indicates the total count of goals scored or achieved by an entity in a given context.
-
B.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
C.
sportNumber
Indicates the specific jersey or uniform number associated with an athlete in a sporting context.
-
D.
topScorer
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
-
E.
ownGoalBy
Indicates that a goal was accidentally scored against a team by the specified player (i.e., the player scored an own goal).
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a95b02cd448190be8e3db9a5a7bac0 |
completed | March 5, 2026, 10:29 a.m. |
| PD | Predicate disambiguation | batch_69a907c1cad08190b9728dd557f39aa0 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.