Triple
T16105839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pit Stop |
E390735
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Yen Tan |
E390735
|
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: Yen Tan | Statement: [Pit Stop, screenwriter, Yen Tan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yen Tan Context triple: [Pit Stop, screenwriter, Yen Tan]
-
A.
Yen Tan
chosen
Yen Tan is a Malaysian-born, Texas-based independent filmmaker known for his nuanced, character-driven dramas exploring queer and Asian American experiences.
-
B.
Nantan
Nantan is a city in central Kyoto Prefecture, Japan, known for its rural landscapes, forests, and traditional cultural sites.
-
C.
Zao Town
Zao Town is a rural Japanese town known for its hot springs, ski resorts, and scenic volcanic landscapes in northeastern Honshu.
-
D.
Wakamatsu
Wakamatsu is a ward in the city of Kitakyushu, Japan, known historically as a port and industrial area on the northern coast of Kyushu.
-
E.
Nagaya
Nagaya is a Japanese surname historically borne by various notable figures, including samurai and aristocrats, and remains in use in modern Japan.
- 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.