Triple
T391710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jim Rice |
E8894
|
entity |
| Predicate | careerSluggingPercentage |
P10810
|
FINISHED |
| Object | .502 |
—
|
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: .502 | Statement: [Jim Rice, careerSluggingPercentage, .502]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerSluggingPercentage Context triple: [Jim Rice, careerSluggingPercentage, .502]
-
A.
careerBattingAverage
Indicates the long-term batting performance of a player, calculated as their total hits divided by total at-bats over their entire career.
-
B.
careerHomeRuns
Indicates the total number of home runs an entity has hit over the entire span of their professional career.
-
C.
careerRBIs
Indicates the total number of runs a player has batted in over the course of their entire career.
-
D.
careerStrikeouts
Indicates the total number of batters a pitcher has struck out over the course of their entire professional career.
-
E.
careerHits
Indicates the total number of hits a player has accumulated over the course of their entire professional 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec7492288190bf33c9c869a0710f |
completed | Feb. 28, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69a2e96a8ca48190abbd8de9b02c115c |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2ea2dc3088190a2aeb4496aff3582 |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.