Triple
T19376387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ho |
E484678
|
entity |
| Predicate | usedInRegion |
P908
|
FINISHED |
| Object | Macau |
—
|
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: Macau | Statement: [Ho, usedInRegion, Macau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Macau Context triple: [Ho, usedInRegion, Macau]
-
A.
Macau
chosen
Macau is a Special Administrative Region of China known for its blend of Portuguese and Chinese cultures and its major casino and tourism industry.
-
B.
Macau
Macau is a coastal municipality in the Brazilian state of Rio Grande do Norte, known for its salt production and fishing activities.
-
C.
Magong
Magong is the main urban center and largest city of Taiwan’s Penghu (Pescadores) archipelago, serving as its political, economic, and transportation hub.
-
D.
Hong Kong, China
Hong Kong, China is a major global financial and trading hub and a Special Administrative Region of China located on the southern coast of the country.
-
E.
Taipa
Taipa is a small coastal settlement in New Zealand’s Far North District, known for its sandy beaches and role as a holiday and fishing destination.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a5c05c08190b91cb32fdb79813b |
completed | April 20, 2026, 12:21 p.m. |
Created at: April 10, 2026, 1:35 p.m.