Triple
T11900070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Honest |
E283125
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Scott Harris |
E955704
|
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: Scott Harris | Statement: [Honest, writer, Scott Harris]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Harris Context triple: [Honest, writer, Scott Harris]
-
A.
Scott Harris
chosen
Scott Harris is an American songwriter and producer known for co-writing numerous pop hits for artists such as Shawn Mendes and The Chainsmokers.
-
B.
Ken Harris
Ken Harris was a renowned American animator best known for his influential work on Warner Bros. cartoons, particularly in collaboration with director Chuck Jones.
-
C.
Craig Harris
Craig Harris is an American jazz trombonist and composer known for his innovative work in avant-garde jazz and film scores, including the soundtrack for "Judas and the Black Messiah."
-
D.
Dan Hughes
Dan Hughes is an American basketball coach best known for leading the WNBA’s Seattle Storm to a championship and for his long, successful career coaching multiple WNBA franchises.
-
E.
Ron Hutchinson
Ron Hutchinson is a Northern Irish playwright and screenwriter known for his work in film and television, including adaptations and genre projects in Hollywood.
- 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_69d6ab2a90b08190a4e818821cc93e6d |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8dd16433881909befca9774bdaab4 |
completed | April 10, 2026, 11:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49ce47d488190af7f832e7719a4ce |
completed | May 1, 2026, 12:30 p.m. |
Created at: April 8, 2026, 9:44 p.m.