Triple
T22395771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen Greenblatt |
E553625
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Greenblatt |
—
|
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: Greenblatt | Statement: [Stephen Greenblatt, hasSurname, Greenblatt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greenblatt Context triple: [Stephen Greenblatt, hasSurname, Greenblatt]
-
A.
Greenblatt
chosen
Greenblatt is a Jewish-origin surname borne by various notable individuals in fields such as entertainment, academia, and business.
-
B.
Richard Greenblatt
Richard Greenblatt is an American computer scientist and pioneering hacker best known for his early work in artificial intelligence and contributions to Lisp systems at MIT.
-
C.
Hillegas
Hillegas is a surname most notably associated with Michael Hillegas, the first Treasurer of the United States.
-
D.
Wilder Stevens
Wilder Stevens is a person notable for bearing the given name Wilder.
-
E.
Byron Thames
Byron Thames is an American actor best known for his work as a child and teen performer in film and television during the late 1970s and 1980s.
- 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_69e11e4da7048190b4387d422a9a0de5 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1585e84b081908c95ed3e0d987ed8 |
completed | April 29, 2026, 1:01 a.m. |
Created at: April 16, 2026, 8:45 p.m.