Triple
T17101445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Willie Wood |
E414988
|
entity |
| Predicate | careerPuntReturnYards |
P125929
|
FINISHED |
| Object | 1628 |
—
|
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: 1628 | Statement: [Willie Wood, careerPuntReturnYards, 1628]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerPuntReturnYards Context triple: [Willie Wood, careerPuntReturnYards, 1628]
-
A.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
B.
careerRushingYards
Indicates the total number of rushing yards an entity has accumulated over the entire span of its career.
-
C.
careerYardsPerCarry
Indicates the average number of yards a player gains per rushing attempt over the entirety of their career.
-
D.
careerFumbleRecoveries
Indicates the total number of times an entity has recovered a fumble over the course of their entire career.
-
E.
careerPassingYardsNFL
Indicates the total number of passing yards a player has accumulated over their entire career in the NFL.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc239a088190a776fe0f4361ffc7 |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d6b1b988190a8d6b6fe78c35e59 |
completed | April 18, 2026, 10:31 a.m. |
| PDg | Predicate description generation | batch_69e37542d060819082aa73948eb8ebd4 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:35 a.m.