Triple
T1026568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emmitt Smith |
E22151
|
entity |
| Predicate | careerTotalTouchdowns |
P23519
|
FINISHED |
| Object | 175 |
—
|
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: 175 | Statement: [Emmitt Smith, careerTotalTouchdowns, 175]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerTotalTouchdowns Context triple: [Emmitt Smith, careerTotalTouchdowns, 175]
-
A.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
B.
careerRushingTouchdowns
Indicates the total number of rushing touchdowns a player has scored over the entire span of their career.
-
C.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
-
D.
passingTouchdownsCareer
Indicates the total number of touchdown passes a player has thrown over the course of their entire career.
-
E.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball 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_69a493d6e380819097b384986ffc315c |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7f5e7b48190b26524573c2824ba |
completed | March 1, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69a4b72619cc8190932fdfa0c74dc055 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b7a0d0308190a00192aa9062bdaa |
completed | March 1, 2026, 10:03 p.m. |
Created at: March 1, 2026, 7:41 p.m.