Triple
T32138100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maddie Rooney |
E820825
|
entity |
| Predicate | primaryHobby |
P150988
|
FINISHED |
| Object | playing basketball |
—
|
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: playing basketball | Statement: [Maddie Rooney, primaryHobby, playing basketball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryHobby Context triple: [Maddie Rooney, primaryHobby, playing basketball]
-
A.
favoriteHobby
chosen
Indicates that one entity has another entity as the hobby they enjoy doing the most.
-
B.
primaryInterest
Indicates that one entity is the main or most significant focus of attention, concern, or engagement for another entity.
-
C.
hasNotableHobby
Indicates that an entity engages in a hobby that is distinctive, remarkable, or otherwise noteworthy.
-
D.
formerHobby
Indicates that an activity was previously pursued as a hobby by an entity but is no longer currently practiced as such.
-
E.
primarySport
Indicates the main sport with which an entity (such as a person, team, or organization) is most closely associated or primarily involved.
- 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_69f349039e0c819091c7a7d322e3f46d |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379eaa540819095a1c5d9f3513f9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:30 a.m.