Triple
T27769524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EJ Manuel |
E701701
|
entity |
| Predicate | hasNFLPassingTouchdowns |
P100924
|
FINISHED |
| Object | 19 |
—
|
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: 19 | Statement: [EJ Manuel, hasNFLPassingTouchdowns, 19]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNFLPassingTouchdowns Context triple: [EJ Manuel, hasNFLPassingTouchdowns, 19]
-
A.
ledNFLInPassingTouchdowns
Indicates that the subject was the league leader in passing touchdowns in the NFL for a given season or time period.
-
B.
careerPassingTouchdownsNFL
chosen
Indicates the total number of touchdown passes a player has thrown over the course of their NFL career.
-
C.
careerTotalTouchdowns
Indicates the total number of touchdowns an entity has scored over the entire duration of its career.
-
D.
careerNFLTouchdowns
Indicates the total number of touchdowns a player has scored over the course of their NFL career.
-
E.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
- 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_69f63894e5848190aec428392562ab06 |
completed | May 2, 2026, 5:47 p.m. |
| PD | Predicate disambiguation | batch_69f6370c8c7c8190a02ea82847bb6e76 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 4:33 p.m.