Triple
T694836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Schmidt |
E13872
|
entity |
| Predicate | careerOPS |
P17560
|
FINISHED |
| Object | .908 |
—
|
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: .908 | Statement: [Mike Schmidt, careerOPS, .908]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerOPS Context triple: [Mike Schmidt, careerOPS, .908]
-
A.
careerStrikeouts
Indicates the total number of batters a pitcher has struck out over the course of their entire professional career.
-
B.
careerHits
Indicates the total number of hits a player has accumulated over the course of their entire professional career.
-
C.
sportsCareer
Indicates a relationship where an entity’s professional involvement, roles, or achievements in sports are associated with a particular sport, team, period, or competitive level.
-
D.
careerReceptions
Indicates the total number of receptions a player has made over the course of their entire career.
-
E.
careerSluggingPercentage
Indicates the overall slugging percentage a player has achieved across their entire career.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c3f39c8190a3014df428817492 |
completed | March 1, 2026, 8:25 p.m. |
| PD | Predicate disambiguation | batch_69a49d23e0a08190b08be9d1eff2a1bb |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a49df19c9481909cc9bc33ed7f011b |
completed | March 1, 2026, 8:13 p.m. |
Created at: March 1, 2026, 7:36 p.m.