Triple
T6628088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lethal Weapon (TV series) |
E149852
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Dan Lin |
E83863
|
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: Dan Lin | Statement: [Lethal Weapon (TV series), executiveProducer, Dan Lin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Lin Context triple: [Lethal Weapon (TV series), executiveProducer, Dan Lin]
-
A.
Dan Lin
chosen
Dan Lin is a Taiwanese-American film producer and executive known for overseeing major franchises such as The Lego Movie series and various DC and Sherlock Holmes films.
-
B.
Greg Yaitanes
Greg Yaitanes is an American television director and producer known for his work on high-profile series such as House of the Dragon and House.
-
C.
Matthew Hannam
Matthew Hannam is a Canadian film and television editor known for his work on acclaimed independent films and series.
-
D.
Dan Goor
Dan Goor is an American television writer and producer best known for co-creating the comedy series "Brooklyn Nine-Nine" and his work on shows like "Parks and Recreation."
-
E.
Chris Yeh
Chris Yeh is an entrepreneur, investor, and author best known for co-authoring the business strategy book "Blitzscaling" with Reid Hoffman.
- 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6afa2e4a48190ba3c70013bab14f2 |
completed | March 27, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbeb04348190957b8e5f098b72bf |
completed | March 27, 2026, 6:26 p.m. |
Created at: March 27, 2026, 1:59 p.m.