Triple
T8573449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haason Reddick |
E202984
|
entity |
| Predicate | wearsHelmetNumberInNFL |
P2651
|
FINISHED |
| Object | 7 for Philadelphia Eagles |
—
|
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: 7 for Philadelphia Eagles | Statement: [Haason Reddick, wearsHelmetNumberInNFL, 7 for Philadelphia Eagles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wearsHelmetNumberInNFL Context triple: [Haason Reddick, wearsHelmetNumberInNFL, 7 for Philadelphia Eagles]
-
A.
nflId
Indicates a unique identifier assigned to an NFL player or entity within the dataset, used to distinguish it from all others.
-
B.
jerseyNumber
chosen
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
C.
leagueRetiredNumberBy
Indicates that a sports league has officially retired a specific jersey number in honor of a particular person or entity.
-
D.
hasJerseyNumberRetired
Indicates that an entity has had its jersey number officially retired, typically in recognition of its contributions or achievements.
-
E.
retiredJerseyNumberByTeam
Indicates that a sports team has officially retired a specific jersey number in honor of a player or figure, making it unavailable for future use by team members.
- 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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea458c1081908e79bee2cbf97207 |
completed | March 31, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69cbd11856048190a1ce4b83a38f6965 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:21 p.m.