Triple
T9131807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Newton Thomas Sigel |
E219103
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Sigel
Sigel is a surname most notably associated with American cinematographer Newton Thomas Sigel, known for his work on major Hollywood films.
|
E780237
|
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: Sigel | Statement: [Newton Thomas Sigel, familyName, Sigel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sigel Context triple: [Newton Thomas Sigel, familyName, Sigel]
-
A.
Siegl
Siegl is the surname of Zev Siegl, an American entrepreneur best known as one of the co-founders of Starbucks.
-
B.
Sieg
The Sieg is a river in western Germany that flows through North Rhine-Westphalia and Rhineland-Palatinate before joining the Rhine.
-
C.
Sig Ruman
Sig Ruman was a German-American character actor known for his comedic and often blustery roles in classic Hollywood films, including several collaborations with the Marx Brothers and directors like Ernst Lubitsch.
-
D.
Senger
Senger is a variant form of the surname Singer, commonly found in German-speaking regions.
-
E.
Strelsau
Strelsau is the fictional capital city of the kingdom of Ruritania in Anthony Hope’s adventure novel "The Prisoner of Zenda."
- 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: Sigel Triple: [Newton Thomas Sigel, familyName, Sigel]
Generated description
Sigel is a surname most notably associated with American cinematographer Newton Thomas Sigel, known for his work on major Hollywood films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sigel Target entity description: Sigel is a surname most notably associated with American cinematographer Newton Thomas Sigel, known for his work on major Hollywood films.
-
A.
Siegl
Siegl is the surname of Zev Siegl, an American entrepreneur best known as one of the co-founders of Starbucks.
-
B.
Sieg
The Sieg is a river in western Germany that flows through North Rhine-Westphalia and Rhineland-Palatinate before joining the Rhine.
-
C.
Sig Ruman
Sig Ruman was a German-American character actor known for his comedic and often blustery roles in classic Hollywood films, including several collaborations with the Marx Brothers and directors like Ernst Lubitsch.
-
D.
Senger
Senger is a variant form of the surname Singer, commonly found in German-speaking regions.
-
E.
Strelsau
Strelsau is the fictional capital city of the kingdom of Ruritania in Anthony Hope’s adventure novel "The Prisoner of Zenda."
- 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_69ca83debfc0819095800583e97ab10f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8ceea6c81909f368f12dac1649c |
completed | April 1, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d047dc25208190a720910e6d43199b |
completed | April 3, 2026, 11:06 p.m. |
| NEDg | Description generation | batch_69d049058dec81909854965276252808 |
completed | April 3, 2026, 11:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d049913b7481909ccbaf4999e37c06 |
completed | April 3, 2026, 11:13 p.m. |
Created at: March 30, 2026, 7:18 p.m.