Triple
T9078048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joel Stransky |
E217537
|
entity |
| Predicate | scoringMethodSpecialty |
P44237
|
FINISHED |
| Object | drop goal |
—
|
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: drop goal | Statement: [Joel Stransky, scoringMethodSpecialty, drop goal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoringMethodSpecialty Context triple: [Joel Stransky, scoringMethodSpecialty, drop goal]
-
A.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
B.
scoringType
Indicates the method or criteria by which performance, outcomes, or results are evaluated and assigned a score in a given context.
-
C.
individualScoring
Indicates that a specific individual receives or is assigned a particular score or evaluation in a given context.
-
D.
primaryScoringStyle
chosen
Indicates the main method or approach by which an entity achieves or generates scores or points.
-
E.
uniformSpecialty
Indicates that multiple entities share the same specific specialty, expertise, or area of focus.
- 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_69ca83d6c14c8190bc056d927f00a2a2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc95c7d3688190a4c1c6a92965eae4 |
completed | April 1, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69cc65fa79bc81908b46f05c8bba920f |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:12 p.m.