Triple
T20885027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abella |
E514254
|
entity |
| Predicate | developedBy |
P73
|
FINISHED |
| Object | Dale Miller |
—
|
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: Dale Miller | Statement: [Abella, developedBy, Dale Miller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dale Miller Context triple: [Abella, developedBy, Dale Miller]
-
A.
Dale Miller
chosen
Dale Miller is a prominent logician and computer scientist known for his influential work in proof theory, logic programming, and automated reasoning.
-
B.
Darryl Miller
Darryl Miller is known primarily as the brother of Hall of Fame NBA shooting guard Reggie Miller.
-
C.
Randy Miller
Randy Miller is a film composer best known for scoring movies such as the long-distance running drama "Without Limits."
-
D.
Jim Miller
Jim Miller is an American mixed martial artist and longtime UFC lightweight contender known for his durability, grappling skills, and record number of UFC appearances.
-
E.
Jim Miller
Jim Miller is a film editor known for his work on major Hollywood productions, including the science-fiction comedy "Men in Black."
- 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_69e0b4f733f081908a401c0b7beb0b9f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c67c9d1c81908031eb77c124f119 |
completed | April 21, 2026, 12:36 a.m. |
Created at: April 16, 2026, 12:46 p.m.