Triple
T21993937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tracey Ullman |
E543155
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ullman |
—
|
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: Ullman | Statement: [Tracey Ullman, familyName, Ullman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ullman Context triple: [Tracey Ullman, familyName, Ullman]
-
A.
Ullman
chosen
Ullman is a surname most prominently associated with British-American actress, comedian, and singer Tracey Ullman.
-
B.
Ullmann
Ullmann is a Norwegian actress and film director renowned for her intense performances in Ingmar Bergman’s films and her influential contributions to European cinema.
-
C.
Tanenbaum
Tanenbaum is the surname of Andrew S. Tanenbaum, a prominent computer scientist known for his influential work on operating systems and computer networks.
-
D.
Galvin
Galvin is a surname of Irish origin borne by various notable individuals in fields such as sports, business, and the arts.
-
E.
Abelson
Abelson is a surname most notably associated with Hal Abelson, an American computer scientist and educator known for his work on the Scheme programming language and the textbook "Structure and Interpretation of Computer Programs."
- 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127639bf48190800b3fa3c1527983 |
completed | April 28, 2026, 9:32 p.m. |
Created at: April 16, 2026, 8:17 p.m.