Triple
T17232661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dale Wasserman |
E418280
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Wasserman |
E737976
|
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: Wasserman | Statement: [Dale Wasserman, familyName, Wasserman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wasserman Context triple: [Dale Wasserman, familyName, Wasserman]
-
A.
Wasserman
chosen
Wasserman is a surname of German and Jewish origin borne by numerous notable individuals across entertainment, politics, sports, and other fields.
-
B.
Wesselmann
Wesselmann is a surname most notably associated with Tom Wesselmann, a prominent American Pop Art painter known for his bold, stylized depictions of the nude and everyday consumer objects.
-
C.
Winkleman
Winkleman is a surname most notably associated with British actress Sophie Winkleman and her extended family, which includes media and entertainment figures.
-
D.
Wurman
Wurman is the surname of Richard Saul Wurman, the American architect and graphic designer best known as the founder of the TED conferences.
-
E.
Weissman
Weissman is a surname most prominently associated with Drew Weissman, the Nobel Prize–winning physician-scientist whose work on mRNA technology enabled the development of COVID-19 vaccines.
- 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_69d886d8e96081909870bff6c3d0bf09 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42df9558481909e2e50ae0b02acf7 |
completed | April 19, 2026, 1:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a016760873c8190bab70ad4ca0c6d8e |
completed | May 11, 2026, 5:21 a.m. |
Created at: April 10, 2026, 5:39 a.m.