Triple

T4744428
Position Surface form Disambiguated ID Type / Status
Subject Jerry Orbach E105325 entity
Predicate familyName P18 FINISHED
Object Orbach
Orbach is a surname most famously associated with American actor Jerry Orbach, known for his roles in "Law & Order" and Broadway musicals.
 E465796 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: Orbach | Statement: [Jerry Orbach, familyName, Orbach]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orbach
Context triple: [Jerry Orbach, familyName, Orbach]
  • A. Orly
    Orly is a commune in the southern suburbs of Paris, France, best known for giving its name to the nearby Paris Orly Airport.
  • B. Marianne Ehrlich
    Marianne Ehrlich was the daughter of Nobel Prize–winning German physician and immunologist Paul Ehrlich.
  • C. Vivienne Segal
    Vivienne Segal was an American actress and singer best known as a leading lady of Broadway musicals in the early to mid-20th century.
  • D. Maria Thins
    Maria Thins was a wealthy and devout Catholic woman in Delft best known as the mother-in-law and patron of the Dutch painter Johannes Vermeer.
  • E. Lucile
    Lucile is a feminine given name of Latin origin, commonly associated with the name Lucille and meaning "light."
  • 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: Orbach
Triple: [Jerry Orbach, familyName, Orbach]
Generated description
Orbach is a surname most famously associated with American actor Jerry Orbach, known for his roles in "Law & Order" and Broadway musicals.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orbach
Target entity description: Orbach is a surname most famously associated with American actor Jerry Orbach, known for his roles in "Law & Order" and Broadway musicals.
  • A. Orly
    Orly is a commune in the southern suburbs of Paris, France, best known for giving its name to the nearby Paris Orly Airport.
  • B. Marianne Ehrlich
    Marianne Ehrlich was the daughter of Nobel Prize–winning German physician and immunologist Paul Ehrlich.
  • C. Vivienne Segal
    Vivienne Segal was an American actress and singer best known as a leading lady of Broadway musicals in the early to mid-20th century.
  • D. Maria Thins
    Maria Thins was a wealthy and devout Catholic woman in Delft best known as the mother-in-law and patron of the Dutch painter Johannes Vermeer.
  • E. Lucile
    Lucile is a popular 1860 verse novel by British writer Edward Bulwer-Lytton, known for its romantic plot and melodramatic style.
  • 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_69bd43ef87a48190a5bc3600711aa032 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd64aa72c0819082ede0f531d75e65 completed March 20, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69be3a37a77881909d32027f1ada99c5 completed March 21, 2026, 6:27 a.m.
NEDg Description generation batch_69be3b11371081908c028d7a1376f473 completed March 21, 2026, 6:30 a.m.
NED2 Entity disambiguation (via description) batch_69be3b8c77f88190aac16b6941fb5df7 completed March 21, 2026, 6:32 a.m.
Created at: March 20, 2026, 1:19 p.m.