Triple
T36532398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herb Adderley |
E900479
|
entity |
| Predicate | numberOfInterceptions |
P20504
|
FINISHED |
| Object | 48 |
—
|
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: 48 | Statement: [Herb Adderley, numberOfInterceptions, 48]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInterceptions Context triple: [Herb Adderley, numberOfInterceptions, 48]
-
A.
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.
-
B.
interceptionsInNFL
chosen
Indicates the number of passes a player has intercepted while playing in the NFL.
-
C.
interceptedBy
Indicates that an action, communication, or movement is stopped, captured, or diverted by another agent before reaching its intended target or destination.
-
D.
interceptionTimeRemaining
Indicates the amount of time left before an interception event between entities is expected to occur.
-
E.
interceptionReturnTouchdowns
Indicates the number of times a defensive player returns an intercepted pass into the opponent’s end zone for a touchdown.
- 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_69f76e5fbb388190b70c4c15573c8143 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:11 p.m.