Triple
T23252879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald "Deke" Slayton |
E581781
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Slayton |
—
|
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: Slayton | Statement: [Donald "Deke" Slayton, familyName, Slayton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Slayton Context triple: [Donald "Deke" Slayton, familyName, Slayton]
-
A.
Slayton
chosen
Slayton is a surname most notably associated with individuals such as Paul Michael Slayton, better known as the American rapper Paul Wall.
-
B.
Lufton
Lufton is a fictional aristocratic family name most notably associated with the character Lord Lufton in Anthony Trollope’s Barsetshire novels.
-
C.
Holderness
Holderness is a small New Hampshire town in the Lakes Region, best known as the home of Squam Lake and the setting for the film "On Golden Pond."
-
D.
Holderness
Holderness is a low-lying coastal region in the East Riding of Yorkshire, England, known for its rapidly eroding North Sea coastline and agricultural landscape.
-
E.
Tilghman
Tilghman is a surname most notably associated with Shirley M. Tilghman, a prominent molecular biologist and former president of Princeton University.
- 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_69e24606b17c81908aba1a4911c8a8ba |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f193f840dc819098e52272abefe616 |
completed | April 29, 2026, 5:15 a.m. |
Created at: April 17, 2026, 4:11 p.m.