Triple
T14653634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Lovers and a Bear |
E344053
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Kim Nguyen |
E1112012
|
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: Kim Nguyen | Statement: [Two Lovers and a Bear, screenwriter, Kim Nguyen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Nguyen Context triple: [Two Lovers and a Bear, screenwriter, Kim Nguyen]
-
A.
Kim Nguyen
chosen
Kim Nguyen is a Canadian film director and screenwriter known for his visually distinctive, character-driven dramas such as the Oscar-nominated "War Witch."
-
B.
Ngoc Le
Ngoc Le is an actor known for appearing in Stanley Kubrick’s Vietnam War film "Full Metal Jacket."
-
C.
Oanh Nguyen
Oanh Nguyen is an actor known for appearing in the film "Two Brothers."
-
D.
Bao Nguyen
Bao Nguyen is a Vietnamese American filmmaker known for his acclaimed documentaries and contributions to Asian American and international cinema.
-
E.
Diane Nguyen
Diane Nguyen is a thoughtful, socially conscious Vietnamese-American writer and one of the main characters in the animated series "BoJack Horseman."
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb518f7dc8190877997ea4cd3eed2 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde17283608190a8351b366cac5e4f |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:27 a.m.