Triple
T27696367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reche Caldwell |
E698303
|
entity |
| Predicate | totalReceivingTouchdowns |
P10747
|
FINISHED |
| Object | 11 |
—
|
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: 11 | Statement: [Reche Caldwell, totalReceivingTouchdowns, 11]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalReceivingTouchdowns Context triple: [Reche Caldwell, totalReceivingTouchdowns, 11]
-
A.
touchdownReceptionLeaderForTeam
Indicates the player who has recorded the most touchdown receptions for a given team.
-
B.
ledLeagueInReceivingTouchdowns
Indicates that the subject had the highest number of receiving touchdowns in the league for a given season or time period.
-
C.
careerTotalTouchdowns
Indicates the total number of touchdowns an entity has scored over the entire duration of its career.
-
D.
careerReceivingTouchdowns
chosen
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
E.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
- 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_69ef590ea74081908f0cd7500d85fa27 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f674e06c9481909ed0ea736408f0d7 |
completed | May 2, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69f673c2f81c8190bf369226306eef09 |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 27, 2026, 2:54 p.m.