Triple
T35630448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Local government areas of Tasmania |
E1029569
|
entity |
| Predicate | hasNumberOfLGAs |
P41115
|
FINISHED |
| Object | 29 |
—
|
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: 29 | Statement: [Local government areas of Tasmania, hasNumberOfLGAs, 29]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfLGAs Context triple: [Local government areas of Tasmania, hasNumberOfLGAs, 29]
-
A.
numberOfLocalGovernmentAreas
chosen
Indicates the count of local government areas associated with a given entity or region.
-
B.
hasLocalGovernmentAreas
Indicates that an entity is administratively divided into, or associated with, one or more local government areas.
-
C.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
D.
hasNumberOfAdministrativeTerritorialEntities
Indicates the count of administrative territorial units (such as states, provinces, or districts) that an entity is divided into or comprises.
-
E.
hasNumberOfSubdistricts
Indicates the relationship specifying how many subdistricts are associated with a given 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_69f76e07bb0c8190968ea2d836fc42c9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff0214d7348190904688376df99bce |
completed | May 9, 2026, 9:44 a.m. |
| PD | Predicate disambiguation | batch_69feffd62fec8190a855922c8b3c57cf |
completed | May 9, 2026, 9:35 a.m. |
Created at: May 3, 2026, 4:05 p.m.