Triple
T26947024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taungurung people |
E678673
|
entity |
| Predicate | traditionalTownOrArea |
P59512
|
FINISHED |
| Object | Seymour |
—
|
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: Seymour | Statement: [Taungurung people, traditionalTownOrArea, Seymour]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalTownOrArea Context triple: [Taungurung people, traditionalTownOrArea, Seymour]
-
A.
traditionalSettlementArea
chosen
Indicates that an area is recognized as a traditional settlement zone associated with a particular group or community.
-
B.
traditionalSettlement
Indicates that an entity is a settlement characterized by long-established, customary, or historically rooted patterns of habitation and land use.
-
C.
traditionalDistrict
Indicates that an entity is located in, associated with, or belongs to a historically recognized or customary administrative or cultural district.
-
D.
popularTown
Indicates that a town is widely liked, frequently visited, or well-regarded by many people.
-
E.
traditionalInCity
Indicates that something is customary, long-established, or historically rooted within a particular city.
- 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_69eeeb4d69588190a7c912164a1c37b3 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f7979a073881909a4fde2558e6b6f3 |
completed | May 3, 2026, 6:44 p.m. |
| PD | Predicate disambiguation | batch_69f7961550f88190b7bb8a9155458b54 |
completed | May 3, 2026, 6:38 p.m. |
Created at: April 27, 2026, 6:22 a.m.