Triple
T36987795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dixieland Band |
E915009
|
entity |
| Predicate | geographicFocusOfCareer |
P192254
|
FINISHED |
| Object | North America |
—
|
NE NERFINISHED |
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: North America | Statement: [Dixieland Band, geographicFocusOfCareer, North America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geographicFocusOfCareer Context triple: [Dixieland Band, geographicFocusOfCareer, North America]
-
A.
geographicScopeOfCareer
chosen
Indicates the geographic area or region within which an individual’s career activities primarily took place or were focused.
-
B.
studCareerLocation
Indicates the place where a student's academic or professional career activities occur or are based.
-
C.
secondaryGeographicFocus
Indicates a secondary or less primary geographic area that is also a focus or target of the entity’s activities, influence, or relevance.
-
D.
focusCityRole
Indicates that a city serves a particular primary role or function within a broader geographic or organizational context.
-
E.
hasOccupationFocus
Indicates that an entity’s occupation is primarily centered on, or specialized in, a particular field, role, or area of activity.
- 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_69f76e8dd0408190b8b46da118ea5128 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd2a215d6c8190a1a428ccaee603f1 |
completed | May 8, 2026, 12:11 a.m. |
| PD | Predicate disambiguation | batch_69fd28ef19688190bb8370f2812a43e7 |
completed | May 8, 2026, 12:06 a.m. |
Created at: May 3, 2026, 4:14 p.m.