Triple
T2681472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Australian government |
E56582
|
entity |
| Predicate | numberOfMainlandTerritories |
P42209
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Australian government, numberOfMainlandTerritories, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMainlandTerritories Context triple: [Australian government, numberOfMainlandTerritories, 2]
-
A.
numberOfUnionTerritories
Indicates the total count of union territories associated with a given country or administrative entity.
-
B.
numberOfTerritories
Indicates the total count of territories associated with a given entity.
-
C.
hasNumberOfDependentTerritories
Indicates the quantitative relationship specifying how many dependent territories are associated with a given entity.
-
D.
hasOverseasTerritory
Indicates that one entity possesses or controls a territory located outside its own primary geographic or sovereign domain.
-
E.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69ab4a4b13fc81909dfdb3f23da46832 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abda2f7bf88190a1e3103dd014d871 |
completed | March 7, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69abd81ab9d08190b72b6104c6dbc769 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abda2dc5788190b4b83cb9ed08266c |
completed | March 7, 2026, 7:56 a.m. |
Created at: March 6, 2026, 9:54 p.m.