Triple
T35517819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2002–03 Premier League |
E1026464
|
entity |
| Predicate | mostPointsTotal |
P17024
|
FINISHED |
| Object | 83 |
—
|
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: 83 | Statement: [2002–03 Premier League, mostPointsTotal, 83]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mostPointsTotal Context triple: [2002–03 Premier League, mostPointsTotal, 83]
-
A.
totalPointsScored
chosen
Indicates the total number of points accumulated or scored by an entity over a defined period, event, or context.
-
B.
topScorerPoints
Indicates the number of points scored by the top-scoring entity in a given context or event.
-
C.
mostPointsPerGamePlayer
Indicates the player who has the highest average number of points scored per game within a given context or season.
-
D.
maxPointsPerGame
Indicates the maximum number of points an entity can score or is allowed to score in a single game.
-
E.
hasHighestPoints
Indicates that the subject entity possesses the greatest number of points compared to all relevant others in the given context.
- 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_69f76dfe78b081908e2b14cb88dd8c00 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79a54aa3c8190b2bb5d790b2d42d4 |
completed | May 3, 2026, 6:56 p.m. |
| PD | Predicate disambiguation | batch_69f7961970408190b669cc556e30a608 |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:04 p.m.