Triple
T8626593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wong Kar-wai |
E204293
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Wong Yi-man |
E747640
|
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: Wong Yi-man | Statement: [Wong Kar-wai, hasChild, Wong Yi-man]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wong Yi-man Context triple: [Wong Kar-wai, hasChild, Wong Yi-man]
-
A.
Chan Wing-yan
Chan Wing-yan is the undercover police officer protagonist in the Hong Kong crime thriller series "Infernal Affairs," known for infiltrating the triads at great personal cost.
-
B.
Yeung Ku-wan
Yeung Ku-wan was a late Qing dynasty Chinese revolutionary leader who played a key role in early anti-imperial movements and helped lay the groundwork for the 1911 Revolution.
-
C.
Chan Kwong-wing
Chan Kwong-wing is a Hong Kong film composer best known for his scores for acclaimed movies such as the Infernal Affairs trilogy.
-
D.
Chan Yi-kan
chosen
Chan Yi-kan is the wife of acclaimed Hong Kong film director Wong Kar-wai.
-
E.
Savio Kwan
Savio Kwan is a business executive best known for his leadership roles at Alibaba Group, where he helped guide the company’s early growth and international expansion.
- 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc472b8fa481909f52f83ea210483e |
completed | March 31, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cecc9abd1081909d45af7498ec7c34 |
completed | April 2, 2026, 8:07 p.m. |
Created at: March 30, 2026, 6:26 p.m.