Triple
T20173598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Willie Brown |
E492031
|
entity |
| Predicate | interceptionsCareer |
P20504
|
FINISHED |
| Object | 54 |
—
|
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: 54 | Statement: [Willie Brown, interceptionsCareer, 54]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interceptionsCareer Context triple: [Willie Brown, interceptionsCareer, 54]
-
A.
interceptionsInNFL
chosen
Indicates the number of passes a player has intercepted while playing in the NFL.
-
B.
interceptions
Indicates that one entity successfully stops, seizes, or cuts off another entity or action in progress, preventing it from reaching its intended target or outcome.
-
C.
interceptionReturnTouchdowns
Indicates the number of times a defensive player returns an intercepted pass into the opponent’s end zone for a touchdown.
-
D.
seasonLedLeagueInInterceptions
Indicates that, in the specified season, the subject player recorded the most interceptions in the league.
-
E.
interceptionsByTitansDefense
Indicates that the Tennessee Titans' defensive unit successfully intercepted passes from the opposing offense.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6684a33688190b22cfc16907e76bc |
completed | April 20, 2026, 5:54 p.m. |
| PD | Predicate disambiguation | batch_69e55b0c11cc8190836d1eee5945f000 |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:36 p.m.