Triple

T6498984
Position Surface form Disambiguated ID Type / Status
Subject Victor Kugler E148829 entity
Predicate spouse P13 FINISHED
Object Laura Kugler
Laura Kugler was the wife of Victor Kugler, one of the helpers who hid Anne Frank and her family during World War II.
E610030 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: Laura Kugler | Statement: [Victor Kugler, spouse, Laura Kugler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Kugler
Context triple: [Victor Kugler, spouse, Laura Kugler]
  • A. Elizabeth Berkley
    Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
  • B. Elizabeth McGovern
    Elizabeth McGovern is an American actress and musician best known for her roles in films like "Ragtime" and the television series "Downton Abbey."
  • C. Téa Leoni
    Téa Leoni is an American actress and producer best known for her leading roles in film and television, including the political drama series "Madam Secretary."
  • D. Jorja Fox
    Jorja Fox is an American actress best known for her long-running role as Sara Sidle on the television series CSI: Crime Scene Investigation.
  • E. Tyne Daly
    Tyne Daly is an American actress acclaimed for her powerful performances in television dramas, film, and theater, including her iconic role in the series "Cagney & Lacey."
  • 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: Laura Kugler
Triple: [Victor Kugler, spouse, Laura Kugler]
Generated description
Laura Kugler was the wife of Victor Kugler, one of the helpers who hid Anne Frank and her family during World War II.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Laura Kugler
Target entity description: Laura Kugler was the wife of Victor Kugler, one of the helpers who hid Anne Frank and her family during World War II.
  • A. Elizabeth Berkley
    Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
  • B. Elizabeth McGovern
    Elizabeth McGovern is an American actress and musician best known for her roles in films like "Ragtime" and the television series "Downton Abbey."
  • C. Téa Leoni
    Téa Leoni is an American actress and producer best known for her leading roles in film and television, including the political drama series "Madam Secretary."
  • D. Jorja Fox
    Jorja Fox is an American actress best known for her long-running role as Sara Sidle on the television series CSI: Crime Scene Investigation.
  • E. Tyne Daly
    Tyne Daly is an American actress acclaimed for her powerful performances in television dramas, film, and theater, including her iconic role in the series "Cagney & Lacey."
  • 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_69c687e9ad288190bae5bcac9c8ac855 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c68ad00d10819096c43f311388fa3a completed March 27, 2026, 1:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6eec89dc881908ca7a8a8849b87f7 completed March 27, 2026, 8:55 p.m.
NEDg Description generation batch_69c6f09ea58c8190bfd8a183581b5a5a completed March 27, 2026, 9:03 p.m.
NED2 Entity disambiguation (via description) batch_69c6f1a0935881908afc30ce76bdf76f completed March 27, 2026, 9:07 p.m.
Created at: March 27, 2026, 1:41 p.m.