Triple
T1601644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Team LeBron players wore number 2 jerseys in honor of Gianna Bryant |
E34405
|
entity |
| Predicate | jerseyNumberWorn |
P2651
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Team LeBron players wore number 2 jerseys in honor of Gianna Bryant, jerseyNumberWorn, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jerseyNumberWorn Context triple: [Team LeBron players wore number 2 jerseys in honor of Gianna Bryant, jerseyNumberWorn, 2]
-
A.
jerseyNumber
chosen
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
B.
wearsJerseyFor
Indicates that one entity wears a jersey representing, belonging to, or in support of another entity (such as a team, organization, or individual).
-
C.
leaderJerseyColor
Indicates the color of the jersey worn by the current leader in a competition or ranking.
-
D.
leaderJerseyName
Indicates the name of the jersey worn by the current leader in a competition or ranking.
-
E.
leagueRetiredNumberBy
Indicates that a sports league has officially retired a specific jersey number in honor of a particular person or entity.
- 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_69a885fea6a481909fe83ba6441f1774 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a95b02cd448190be8e3db9a5a7bac0 |
completed | March 5, 2026, 10:29 a.m. |
| PD | Predicate disambiguation | batch_69a907c1cad08190b9728dd557f39aa0 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.