Triple
T4777023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bailey |
E106075
|
entity |
| Predicate | jerseyNumberMeaning |
P33050
|
FINISHED |
| Object | reference to 72 degrees and sunny in Los Angeles |
—
|
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: reference to 72 degrees and sunny in Los Angeles | Statement: [Bailey, jerseyNumberMeaning, reference to 72 degrees and sunny in Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jerseyNumberMeaning Context triple: [Bailey, jerseyNumberMeaning, reference to 72 degrees and sunny in Los Angeles]
-
A.
reasonForJerseyNumber
chosen
Indicates the explanation or motivation behind an entity’s choice of a particular jersey number.
-
B.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
C.
jerseyNumberStyle
Indicates the visual design or formatting style used for displaying a player’s jersey number.
-
D.
jerseyNumberRules
Indicates rules or constraints governing which jersey numbers may be assigned or used.
-
E.
jerseyNumberCoached
Indicates that a coach was responsible for coaching a player (or players) who wore a specific jersey number.
- 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_69bd43f3074c8190937e7b0a457fe9f1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd69237f80819090713ed62653fb75 |
completed | March 20, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69bd622be1388190ab5511b589c878c0 |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:21 p.m.