Triple
T2666633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jason Taylor |
E55650
|
entity |
| Predicate | interceptionReturnTouchdowns |
P41546
|
FINISHED |
| Object | multiple |
—
|
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 | Statement: [Jason Taylor, interceptionReturnTouchdowns, multiple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interceptionReturnTouchdowns Context triple: [Jason Taylor, interceptionReturnTouchdowns, multiple]
-
A.
interceptedQuarterback
Indicates that a defensive player successfully caught a pass thrown by the quarterback, resulting in an interception.
-
B.
interceptionsInNFL
Indicates the number of passes a player has intercepted while playing in the NFL.
-
C.
interceptionYardLine
Indicates the yard line on the field where an interception occurs during a play.
-
D.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
-
E.
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.
- 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_69ab49e54de48190be708cd1cf8be073 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd97040e48190b0a87489f108810e |
completed | March 7, 2026, 7:53 a.m. |
| PD | Predicate disambiguation | batch_69abd8190ad481908f3e14ac84d0940a |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8f98c348190a68064c565589459 |
completed | March 7, 2026, 7:51 a.m. |
Created at: March 6, 2026, 9:54 p.m.