Triple
T5356102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Siegel |
E102695
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Siegel |
E21266
|
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: Siegel | Statement: [Don Siegel, familyName, Siegel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siegel Context triple: [Don Siegel, familyName, Siegel]
-
A.
Siegel
chosen
Siegel is the surname of Benjamin "Bugsy" Siegel, the infamous American mobster who played a key role in the development of Las Vegas.
-
B.
Mace Siegel
Mace Siegel was an American real estate developer and businessman best known as a co-founder and longtime leader of The Macerich Company, one of the largest shopping center owners in the United States.
-
C.
Eisenberg
Eisenberg is a surname most notably associated with American actress Hallie Kate Eisenberg.
-
D.
Schatzberg
Schatzberg is a surname most notably associated with Jerry Schatzberg, an American photographer and film director known for works like "Panic in Needle Park" and "Scarecrow."
-
E.
Silverberg
Silverberg is a surname most notably associated with American science fiction author Robert Silverberg.
- 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_69bd43d8f7248190b64c140734b5c9a8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd862f0ea48190bec78690ab3bee51 |
completed | March 20, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21e2b7b08190aca4c2855ff041de |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:01 p.m.