Triple
T12494750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SATAN |
E298654
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Dan Farmer |
E987498
|
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: Dan Farmer | Statement: [SATAN, developer, Dan Farmer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Farmer Context triple: [SATAN, developer, Dan Farmer]
-
A.
Dan Farmer
chosen
Dan Farmer is a prominent computer security expert best known for pioneering network vulnerability assessment tools and advancing Unix and Internet security practices.
-
B.
David Healy
David Healy is a sensitive, artistic young man and Darlene Conner’s long-term love interest and eventual husband on the sitcom "Roseanne" and its spin-off "The Conners."
-
C.
David Healy
David Healy is a former Northern Ireland international footballer who became a successful manager in the Irish League.
-
D.
John Schuck
John Schuck is an American character actor known for his work in film, television, and theater, including notable roles in productions such as "M*A*S*H," "McMillan & Wife," and the "Star Trek" film series.
-
E.
Dan Kircher
Dan Kircher is a film editor known for his work on feature films such as the horror-comedy "Come to Daddy."
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94de4089c8190917a45365e641437 |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6685bafcc8190beae748d979762e1 |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 8, 2026, 9:56 p.m.