Triple
T15224231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve Bartman |
E363834
|
entity |
| Predicate | jerseyNumberWornAtGame |
P117622
|
FINISHED |
| Object | Cubs cap and headphones, no jersey number |
—
|
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: Cubs cap and headphones, no jersey number | Statement: [Steve Bartman, jerseyNumberWornAtGame, Cubs cap and headphones, no jersey number]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jerseyNumberWornAtGame Context triple: [Steve Bartman, jerseyNumberWornAtGame, Cubs cap and headphones, no jersey number]
-
A.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
B.
jerseyNumberTeam
Indicates the association between a specific jersey number and the team for which that jersey number is used or assigned.
-
C.
jerseyNumberStyle
Indicates the visual design or formatting style used for displaying a player’s jersey number.
-
D.
jerseyNumberCoached
Indicates that a coach was responsible for coaching a player (or players) who wore a specific jersey number.
-
E.
jerseyNumberManaged
Indicates that an entity is responsible for assigning, organizing, or overseeing jersey numbers for players or team members.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078a9318819081db3b7bcc28e04f |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca8479188190b2e5d3bc708d7d07 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2ca6148190967c319728ec3661 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:12 a.m.