Triple
T18023448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brendan O’Hare |
E431185
|
entity |
| Predicate | roleInTeenageFanclub |
P74878
|
FINISHED |
| Object | drummer |
—
|
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: drummer | Statement: [Brendan O’Hare, roleInTeenageFanclub, drummer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInTeenageFanclub Context triple: [Brendan O’Hare, roleInTeenageFanclub, drummer]
-
A.
roleInClub
chosen
Indicates that an entity holds a specific position or function within a particular club.
-
B.
roleInNeverland
Indicates that an entity has a specific role, function, or position within the fictional setting of Neverland.
-
C.
roleAtSportsTeam
Indicates the specific position or function an individual holds within a sports team.
-
D.
roleInFranchiseHistory
Indicates the specific function, position, or contribution an entity has within the historical development or timeline of a franchise.
-
E.
roleInFrancesHa
Indicates that one entity plays a specific role or character in the film "Frances Ha" in relation to another 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_69d8b9050fb48190890155145deb0a66 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9c475dc819086c4cd86d663791f |
completed | April 19, 2026, 11:17 a.m. |
| PD | Predicate disambiguation | batch_69e3f904b8048190add43883cd7cb191 |
completed | April 18, 2026, 9:35 p.m. |
Created at: April 10, 2026, 10:24 a.m.