Triple
T8530557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Hutson |
E201933
|
entity |
| Predicate | ledLeagueInTouchdownReceptions |
P69376
|
FINISHED |
| Object | multiple seasons |
—
|
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: multiple seasons | Statement: [Don Hutson, ledLeagueInTouchdownReceptions, multiple seasons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ledLeagueInTouchdownReceptions Context triple: [Don Hutson, ledLeagueInTouchdownReceptions, multiple seasons]
-
A.
ledLeagueInReceivingTouchdowns
chosen
Indicates that the subject had the highest number of receiving touchdowns in the league for a given season or time period.
-
B.
ledLeagueInReceivingYards
Indicates that the subject had the highest total receiving yards in the league for a given season or time period.
-
C.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
D.
sportNumberOfReceptionsNFL
Indicates the number of receptions a player has made in NFL games.
-
E.
careerNFLTouchdowns
Indicates the total number of touchdowns a player has scored over the course of their NFL career.
- 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_69ca83228b24819085d22e7dc99f5d94 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe67546248190b359c845c0161ad3 |
completed | March 31, 2026, 3:21 p.m. |
| PD | Predicate disambiguation | batch_69cbd111bf988190be98c92a607c6456 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:17 p.m.