Triple
T18333689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marshall Bell |
E439212
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Marshall Bell |
—
|
NE NERFINISHED |
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: Marshall Bell | Statement: [Marshall Bell, name, Marshall Bell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marshall Bell Context triple: [Marshall Bell, name, Marshall Bell]
-
A.
Marshall Bell
chosen
Marshall Bell is an American character actor known for his memorable supporting roles in films such as Total Recall, Stand by Me, and A Nightmare on Elm Street 2.
-
B.
Marshall Harvey
Marshall Harvey is a film editor best known for his work on movies such as the dark comedy "The 'Burbs."
-
C.
Maurice Bellamy
Maurice Bellamy is a notable alumnus of Kathleen High School, recognized for his achievements after graduating from the institution.
-
D.
Marshall Adams
Marshall Adams is a cinematographer best known for his work on the television series "Better Call Saul" and the film "El Camino: A Breaking Bad Movie."
-
E.
Marshall Pease
Marshall Pease is a computer scientist best known for co-authoring the seminal paper that introduced the Byzantine Generals Problem in distributed computing and fault tolerance.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9175fec8190af865699b4e64d8c |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50ecbc76c8190a80c0c8c8bce1cbd |
completed | April 19, 2026, 5:20 p.m. |
Created at: April 10, 2026, 10:36 a.m.