Triple
T4765953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenny Lofton |
E105809
|
entity |
| Predicate | careerStolenBasesApprox |
P20801
|
FINISHED |
| Object | 600+ |
—
|
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: 600+ | Statement: [Kenny Lofton, careerStolenBasesApprox, 600+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerStolenBasesApprox Context triple: [Kenny Lofton, careerStolenBasesApprox, 600+]
-
A.
careerStolenBases
chosen
Indicates the total number of bases a player successfully stole over the entire span of their playing career.
-
B.
careerSluggingPercentage
Indicates the overall slugging percentage a player has achieved across their entire career.
-
C.
careerOnBasePercentage
Indicates a player's on-base percentage averaged over the entire duration of their career.
-
D.
careerBattingAverage
Indicates the long-term batting performance of a player, calculated as their total hits divided by total at-bats over their entire career.
-
E.
careerHomeRuns
Indicates the total number of home runs an entity has hit over the entire span of their professional career.
- 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_69bd43f226fc8190b867cc249c2a9042 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd686ef1b08190ad60375592c9d6c0 |
completed | March 20, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69bd622807f881908e4bcb14f7731bac |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:21 p.m.