Triple
T260851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Havlicek |
E5536
|
entity |
| Predicate | scoredOverPointsCareer |
P10050
|
FINISHED |
| Object | 26000+ |
—
|
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: 26000+ | Statement: [John Havlicek, scoredOverPointsCareer, 26000+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoredOverPointsCareer Context triple: [John Havlicek, scoredOverPointsCareer, 26000+]
-
A.
careerRunsScored
Indicates the total number of runs an entity has scored over the entire duration of their playing career.
-
B.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
-
C.
topScorer
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
-
D.
careerHomeRuns
Indicates the total number of home runs an entity has hit over the entire span of their professional career.
-
E.
notablePlayoffStatistic
Indicates that there exists a significant or distinguished statistical achievement associated with an entity’s performance in playoff competition.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e2aba74819093eddd8d820260c0 |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b6c968c819094fc903a3a377e15 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25e292fdc8190bfd51d8848f9ed58 |
completed | Feb. 28, 2026, 3:16 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.