Triple
T14082080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Time |
E338891
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Matthew Thiessen |
E851034
|
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: Matthew Thiessen | Statement: [Good Time, writer, Matthew Thiessen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Thiessen Context triple: [Good Time, writer, Matthew Thiessen]
-
A.
Matt Thiessen
chosen
Matt Thiessen is an American musician best known as the lead vocalist, primary songwriter, and guitarist for the Christian rock band Relient K.
-
B.
Matthew Jensen
Matthew Jensen is a cinematographer best known for his work on major films such as the 2017 superhero movie "Wonder Woman."
-
C.
Chris Sievernich
Chris Sievernich is a German film producer best known for his work on acclaimed art-house and independent films, including Wim Wenders’ "Paris, Texas."
-
D.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
-
E.
Matthew Shafer
Matthew Shafer, better known by his stage name Uncle Kracker, is an American singer-songwriter and musician recognized for his blend of rock, country, and pop influences.
- 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_69d81c687b0c819087fd9ed4198403f8 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5c5f759c81909bfd60ab35b0937b |
completed | April 14, 2026, 3:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe249c57bc819089baed544fb8fead |
completed | May 8, 2026, 5:59 p.m. |
Created at: April 9, 2026, 10:21 p.m.