Triple
T16105915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chan Is Missing |
E390737
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Wood Moy |
E1195140
|
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: Wood Moy | Statement: [Chan Is Missing, castMember, Wood Moy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wood Moy Context triple: [Chan Is Missing, castMember, Wood Moy]
-
A.
Wood Moy
chosen
Wood Moy was a Chinese American actor best known for his pioneering role in Asian American cinema, particularly through his performance in the landmark independent film "Chan Is Missing."
-
B.
Norsey Wood
Norsey Wood is an ancient woodland and designated Local Nature Reserve near Billericay in Essex, known for its rich biodiversity and archaeological features.
-
C.
Wood End
Wood End is a residential neighborhood located within the town of Hayes in west London, England.
-
D.
Oaken
Oaken is a friendly shopkeeper and sauna owner from Disney's Frozen franchise, known for his cheerful demeanor and memorable "Yoo-hoo!" greeting.
-
E.
Roundwood
Roundwood is a residential area or neighborhood within the town of Harpenden in Hertfordshire, England.
- 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff6d81d081909e1315f4dbfd7369 |
completed | April 17, 2026, 9:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff2a16acc8190be9ed181c7a44def |
completed | May 10, 2026, 2:51 a.m. |
Created at: April 10, 2026, 5 a.m.