Triple
T44628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Australia |
E876
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Sydney |
E8462
|
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: Sydney | Statement: [Australia, largestCity, Sydney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sydney Context triple: [Australia, largestCity, Sydney]
-
A.
Sydney
chosen
Sydney is Australia's largest and most populous city, renowned for its iconic harbour, Opera House, and Harbour Bridge.
-
B.
Melbourne
Melbourne is a major Australian city known for its vibrant arts scene, diverse culture, and status as a leading center for sports and education.
-
C.
Wollongong
Wollongong is a coastal city in Australia known for its heavy industry, port facilities, and popular surf beaches along the Illawarra region.
-
D.
Gold Coast
Gold Coast is an affluent stretch of Fifth Avenue in New York City renowned for its luxury residences, historic mansions, and high-end prestige.
-
E.
Newcastle, New South Wales
Newcastle, New South Wales is a coastal Australian city known for its busy port, beaches, and historic industrial and coal-mining heritage.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24ae36824819080e8336a3c9f9bf8 |
completed | Feb. 28, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a284f9fcd48190a3331f06d5dc00e8 |
completed | Feb. 28, 2026, 6:02 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.