Triple
T5693541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eat Drink Man Woman |
E125481
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Hui-Ling Wang |
E539298
|
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: Hui-Ling Wang | Statement: [Eat Drink Man Woman, screenwriter, Hui-Ling Wang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hui-Ling Wang Context triple: [Eat Drink Man Woman, screenwriter, Hui-Ling Wang]
-
A.
Hui-Ling Wang
chosen
Hui-Ling Wang is a Taiwanese screenwriter best known for co-writing the acclaimed Ang Lee films "Lust, Caution" and "Crouching Tiger, Hidden Dragon."
-
B.
Zhong-Ying Wang
Zhong-Ying Wang is a physicist known for collaborative work in theoretical and cosmological physics, including research conducted with Paul Steinhardt.
-
C.
Yanluo Wang
Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
-
D.
Ziyu Wang
Ziyu Wang is a machine learning researcher best known for co-developing the dueling deep Q-network (Dueling DQN) architecture in deep reinforcement learning.
-
E.
Liwen Shao
Liwen Shao is a brilliant and ambitious Chinese businesswoman and technologist in the Pacific Rim universe, known for her pivotal role in developing advanced Jaeger drone technology.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c023e7dbe48190850b501f223614e3 |
completed | March 22, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07dd76f008190970c3b17ec8cbfd8 |
completed | March 22, 2026, 11:40 p.m. |
Created at: March 22, 2026, 3:44 p.m.