Triple
T8483875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mom + Pop |
E200785
|
entity |
| Predicate | hasArtistMember |
P22076
|
FINISHED |
| Object | Peter Lalish |
E247303
|
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: Peter Lalish | Statement: [Mom + Pop, hasArtistMember, Peter Lalish]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Lalish Context triple: [Mom + Pop, hasArtistMember, Peter Lalish]
-
A.
Peter Lalish
chosen
Peter Lalish is a musician best known as a guitarist and member of the indie rock band Lucius.
-
B.
Peter Nashel
Peter Nashel is an American composer known for his film and television scores, including his work on the darkly comedic biopic "I, Tonya."
-
C.
Andrew Sarlo
Andrew Sarlo is a music producer known for his work with indie and alternative artists such as Big Thief and Bon Iver.
-
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_69ca831d7b148190a6e32c1de43ab13b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe539b70c81909f8f045312f0d5f8 |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea83707e481909f9dfd6450a28d3d |
completed | April 2, 2026, 5:32 p.m. |
Created at: March 30, 2026, 6:12 p.m.