Triple
T3826728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinese American cinema |
E88707
|
entity |
| Predicate | notableDirector |
P4744
|
FINISHED |
| Object |
Yen Tan
Yen Tan is a Malaysian-born, Texas-based independent filmmaker known for his nuanced, character-driven dramas exploring queer and Asian American experiences.
|
E390735
|
NE FINISHED |
How this triple was built (4 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: Yen Tan | Statement: [Chinese American cinema, notableDirector, Yen Tan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yen Tan Context triple: [Chinese American cinema, notableDirector, Yen Tan]
-
A.
Nantan
Nantan is a city in central Kyoto Prefecture, Japan, known for its rural landscapes, forests, and traditional cultural sites.
-
B.
Tanabe
Tanabe is a coastal city in Japan known as a gateway to the Kumano Kodo pilgrimage routes and for its scenic natural landscapes.
-
C.
Kamiyama
Kamiyama is a Japanese surname borne by various individuals, including artists, athletes, and public figures.
-
D.
Daikanyama
Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
-
E.
Shirokane
Shirokane is an upscale residential district in Minato, Tokyo, known for its quiet, leafy streets, luxury apartments, and proximity to central business and shopping areas.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Yen Tan Triple: [Chinese American cinema, notableDirector, Yen Tan]
Generated description
Yen Tan is a Malaysian-born, Texas-based independent filmmaker known for his nuanced, character-driven dramas exploring queer and Asian American experiences.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yen Tan Target entity description: Yen Tan is a Malaysian-born, Texas-based independent filmmaker known for his nuanced, character-driven dramas exploring queer and Asian American experiences.
-
A.
Nantan
Nantan is a city in central Kyoto Prefecture, Japan, known for its rural landscapes, forests, and traditional cultural sites.
-
B.
Tanabe
Tanabe is a coastal city in Japan known as a gateway to the Kumano Kodo pilgrimage routes and for its scenic natural landscapes.
-
C.
Kamiyama
Kamiyama is a Japanese surname borne by various individuals, including artists, athletes, and public figures.
-
D.
Daikanyama
Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
-
E.
Shirokane
Shirokane is an upscale residential district in Minato, Tokyo, known for its quiet, leafy streets, luxury apartments, and proximity to central business and shopping areas.
- F. None of above. chosen
Provenance (5 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_69aed9538cf881909d9ce8ca4ac7c18c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb64c72c8190b5f3d376aa4ee933 |
completed | March 9, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb4f41c88190b3040236462c37cc |
completed | March 14, 2026, 6:08 a.m. |
| NEDg | Description generation | batch_69b4fc9fc5dc81908772fc642285abf6 |
completed | March 14, 2026, 6:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4fd0dc3d48190b907d7a1c1e44066 |
completed | March 14, 2026, 6:15 a.m. |
Created at: March 9, 2026, 3:17 p.m.