Triple
T3904428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1999 St. Louis Rams offense |
E90570
|
entity |
| Predicate | pointsPerGame |
P52811
|
FINISHED |
| Object | 32.9 |
—
|
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: 32.9 | Statement: [1999 St. Louis Rams offense, pointsPerGame, 32.9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsPerGame Context triple: [1999 St. Louis Rams offense, pointsPerGame, 32.9]
-
A.
careerPointsPerGame
Indicates the average number of points an individual scores per game over the course of their entire career.
-
B.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
-
C.
careerBlocksPerGame
Indicates the average number of blocked shots a player records per game over the course of their entire career.
-
D.
scoringAverageOver30PPGSeason
Indicates that an entity (typically a player) has recorded a season with a scoring average exceeding 30 points per game.
-
E.
careerAssistsPerGame
Indicates the average number of assists a player records per game over the course of their entire 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_69aed95d315881908cbf1bf4a7215fbf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75cff148190b6d5979d17fae085 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef1aada308190821a3dfa6af170b3 |
completed | March 9, 2026, 4:13 p.m. |
Created at: March 9, 2026, 3:22 p.m.