Triple
T6800208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 金本知憲 |
E156163
|
entity |
| Predicate | 通算打点数 |
P73073
|
FINISHED |
| Object | 1521打点 |
—
|
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: 1521打点 | Statement: [金本知憲, 通算打点数, 1521打点]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 通算打点数 Context triple: [金本知憲, 通算打点数, 1521打点]
-
A.
totalPointsScored
Indicates the total number of points accumulated or scored by an entity over a defined period, event, or context.
-
B.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
-
C.
onBasePlusSlugging
Indicates a relationship where a player’s offensive performance is quantified by combining their on-base percentage with their slugging percentage into a single metric.
-
D.
pointsPerGame
Indicates the average number of points an entity scores per game over a given set of games.
-
E.
battingAverage
Indicates the statistical relationship between a batter’s number of hits and official at-bats, expressing how often they successfully get a hit.
- 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_69c6881844448190a65822d9b39d7f88 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2e457408190a0ad9b0c48d8147c |
completed | March 27, 2026, 6:56 p.m. |
| PD | Predicate disambiguation | batch_69c6d099bf08819089a9f9894d037e74 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d2a8f9188190abbb8c730e7b5edf |
completed | March 27, 2026, 6:55 p.m. |
Created at: March 27, 2026, 2:15 p.m.