Triple
T27769525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EJ Manuel |
E701701
|
entity |
| Predicate | hasNFLInterceptions |
P20504
|
FINISHED |
| Object | 15 |
—
|
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: 15 | Statement: [EJ Manuel, hasNFLInterceptions, 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNFLInterceptions Context triple: [EJ Manuel, hasNFLInterceptions, 15]
-
A.
interceptionsInNFL
chosen
Indicates the number of passes a player has intercepted while playing in the NFL.
-
B.
interceptionsThrown
Indicates the number of times a pass thrown by a player is caught by the opposing team’s defense (i.e., intercepted).
-
C.
seasonLedLeagueInInterceptions
Indicates that, in the specified season, the subject player recorded the most interceptions in the league.
-
D.
interceptionReturnTouchdowns
Indicates the number of times a defensive player returns an intercepted pass into the opponent’s end zone for a touchdown.
-
E.
sportNumberOfReceptionsNFL
Indicates the number of receptions a player has made in NFL games.
- 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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c6895f0819088655277e45859a8 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 4:33 p.m.