Triple
T6082236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeffrey D. Ullman |
E135550
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jeffrey David Ullman |
E135550
|
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: Jeffrey David Ullman | Statement: [Jeffrey D. Ullman, name, Jeffrey David Ullman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey David Ullman Context triple: [Jeffrey D. Ullman, name, Jeffrey David Ullman]
-
A.
Jeremy Shamos
Jeremy Shamos is an American stage and screen actor known for his work on Broadway and in film and television.
-
B.
Jeffrey D. Ullman
chosen
Jeffrey D. Ullman is a prominent American computer scientist known for his foundational contributions to database theory, algorithms, and formal languages, and for coauthoring several classic textbooks in computer science.
-
C.
Michael Lehmann
Michael Lehmann is an American film and television director best known for the dark comedy "Heathers" and various other Hollywood comedies.
-
D.
David Ulevitch
David Ulevitch is an American technology entrepreneur and investor best known as the founder of the DNS and internet security company OpenDNS.
-
E.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
- 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05774bc948190a446b27e83f7079b |
completed | March 22, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d57b5f481908d7df374837a486a |
completed | March 23, 2026, 11 a.m. |
Created at: March 22, 2026, 4:11 p.m.