Triple
T33331217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Bibby |
E853409
|
entity |
| Predicate | jerseyNumberSymbolized |
P176814
|
FINISHED |
| Object | his identity as a player |
—
|
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: his identity as a player | Statement: [Mike Bibby, jerseyNumberSymbolized, his identity as a player]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jerseyNumberSymbolized Context triple: [Mike Bibby, jerseyNumberSymbolized, his identity as a player]
-
A.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
B.
jerseyNumberStyle
Indicates the visual design or formatting style used for displaying a player’s jersey number.
-
C.
jerseyNumberTeam
Indicates the association between a specific jersey number and the team for which that jersey number is used or assigned.
-
D.
jerseyNumberManaged
Indicates that an entity is responsible for assigning, organizing, or overseeing jersey numbers for players or team members.
-
E.
jerseyNumberRules
Indicates rules or constraints governing which jersey numbers may be assigned or used.
- 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_69f34969614c81909cd99661b0902533 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f38159d08190980ad639e08f00f4 |
completed | May 3, 2026, 7:04 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d7bee48190b94e0beb48a1d7fa |
completed | May 3, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69f6f37f36ac8190b1bff8711d6771cb |
completed | May 3, 2026, 7:04 a.m. |
Created at: May 1, 2026, 1:34 a.m.