Triple
T17025447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office (2015 film) |
E413052
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Tang Wei |
E544135
|
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: Tang Wei | Statement: [Office (2015 film), stars, Tang Wei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tang Wei Context triple: [Office (2015 film), stars, Tang Wei]
-
A.
Tang Wei
chosen
Tang Wei is a Chinese actress best known internationally for her breakout leading role in Ang Lee’s espionage thriller film "Lust, Caution."
-
B.
Zhao Tao
Zhao Tao is a renowned Chinese actress best known for her long-term collaboration with director Jia Zhangke in critically acclaimed art-house films.
-
C.
Zhou Dongyu
Zhou Dongyu is a prominent Chinese actress acclaimed for her versatile performances in films such as "Under the Hawthorn Tree" and "Soul Mate," for which she has received multiple major acting awards.
-
D.
Zhou Xun
Zhou Xun is a renowned Chinese actress and singer celebrated for her versatile performances in film and television and regarded as one of the leading figures of contemporary Chinese cinema.
-
E.
Li Bingbing
Li Bingbing is a Chinese actress known internationally for her roles in both Chinese cinema and Hollywood films, including action and science fiction blockbusters.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d46a5081908bc5681621dd8534 |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012334c3b48190b125ab926450c45b |
completed | May 11, 2026, 12:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.