Triple
T10467135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blackhat |
E246825
|
entity |
| Predicate | filmingLocation |
P40
|
FINISHED |
| Object | Hong Kong |
E8492
|
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: Hong Kong | Statement: [Blackhat, filmingLocation, Hong Kong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hong Kong Context triple: [Blackhat, filmingLocation, Hong Kong]
-
A.
Hong Kong, China
chosen
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.
-
B.
Kowloon
Kowloon is a densely populated urban area of Hong Kong known for its vibrant street life, markets, and skyline facing Victoria Harbour.
-
C.
Macau
Macau is a Special Administrative Region of China known for its blend of Portuguese and Chinese cultures and its major casino and tourism industry.
-
D.
Macau
Macau is a coastal municipality in the Brazilian state of Rio Grande do Norte, known for its salt production and fishing activities.
-
E.
Taipei–Hong Kong
Taipei–Hong Kong is a heavily traveled East Asian air route connecting Taiwan’s capital with Hong Kong, served by numerous carriers and popular for both business and tourism.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5092e3230819098ab444f73c9bd40 |
completed | April 7, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90d92510481909135a75b2f582795 |
completed | April 10, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:20 p.m.