Triple
T6875075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balmain |
E158651
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Rozelle |
E232281
|
NE 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: Rozelle | Statement: [Balmain, adjacentTo, Rozelle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rozelle Context triple: [Balmain, adjacentTo, Rozelle]
-
A.
Rozelle
chosen
Rozelle is an inner-west suburb of Sydney, Australia, known for its proximity to the CBD, heritage streetscapes, and educational and health facilities.
-
B.
Arlington
Arlington is a suburban town in Middlesex County, Massachusetts, located northwest of Boston and known for its historic sites and residential character.
-
C.
Arlington
Arlington is a small city in northern Washington State, United States, situated in Snohomish County north of Seattle.
-
D.
Arlington
Arlington is a major city in the Dallas–Fort Worth metropolitan area known for its sports stadiums, entertainment venues, and rapidly growing population.
-
E.
Arlington
Arlington is the middle name of American poet Edwin Arlington Robinson, after whom he is commonly known.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c68832af1481908ce356e133ebaebe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8c9e7b481909079b0f1fb1bc217 |
completed | March 27, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c742b66ca08190bed17fa3ab6733ef |
completed | March 28, 2026, 2:53 a.m. |
Created at: March 27, 2026, 2:22 p.m.