Triple
T171162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benjamin "Bugsy" Siegel |
E3124
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Siegel
Siegel is the surname of Benjamin "Bugsy" Siegel, the infamous American mobster who played a key role in the development of Las Vegas.
|
E21266
|
NE FINISHED |
How this triple was built (4 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: [Benjamin "Bugsy" Siegel, familyName, Siegel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siegel Context triple: [Benjamin "Bugsy" Siegel, familyName, Siegel]
-
A.
Bader
Bader is the maiden surname of Ruth Bader Ginsburg, the late U.S. Supreme Court Justice and pioneering advocate for gender equality.
-
B.
Erwin
Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
-
C.
Miller
Miller is a common English and Scottish occupational surname historically given to people who worked in grain mills.
-
D.
Earl
An Earl is a noble rank in the British and some European peerage systems, historically positioned below a marquess and above a viscount.
-
E.
Hölldobler
Hölldobler is a German surname most notably associated with Bert Hölldobler, a prominent behavioral ecologist and myrmecologist known for his research on ants.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Siegel Triple: [Benjamin "Bugsy" Siegel, familyName, Siegel]
Generated description
Siegel is the surname of Benjamin "Bugsy" Siegel, the infamous American mobster who played a key role in the development of Las Vegas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Siegel Target entity description: Siegel is the surname of Benjamin "Bugsy" Siegel, the infamous American mobster who played a key role in the development of Las Vegas.
-
A.
Bader
Bader is the maiden surname of Ruth Bader Ginsburg, the late U.S. Supreme Court Justice and pioneering advocate for gender equality.
-
B.
Erwin
Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
-
C.
Miller
Miller is a common English and Scottish occupational surname historically given to people who worked in grain mills.
-
D.
Earl
An Earl is a noble rank in the British and some European peerage systems, historically positioned below a marquess and above a viscount.
-
E.
Hölldobler
Hölldobler is a German surname most notably associated with Bert Hölldobler, a prominent behavioral ecologist and myrmecologist known for his research on ants.
- F. None of above. chosen
Provenance (5 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258b94d00819098e90bdfa1306f9f |
completed | Feb. 28, 2026, 2:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2e3c131cc81909092b2aab26cd1ba |
completed | Feb. 28, 2026, 12:46 p.m. |
| NEDg | Description generation | batch_69a2e43c273c8190ac28bb1826f30eb4 |
completed | Feb. 28, 2026, 12:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2e492948c8190a9338cf1a7b5acd8 |
completed | Feb. 28, 2026, 12:50 p.m. |
Created at: Feb. 28, 2026, 2:34 a.m.