Triple

T15066124
Position Surface form Disambiguated ID Type / Status
Subject Lilyan Tashman E379759 entity
Predicate familyName P18 FINISHED
Object Tashman
Tashman is a surname most notably associated with American stage and film actress Lilyan Tashman, a prominent figure of early Hollywood cinema.
E1134657 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: Tashman | Statement: [Lilyan Tashman, familyName, Tashman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tashman
Context triple: [Lilyan Tashman, familyName, Tashman]
  • A. Kiali
    Kiali is an observability and management console for Istio-based service meshes, providing traffic visualization, configuration insight, and troubleshooting tools.
  • B. Tashir
    Tashir is a small town located in Armenia’s northern Lori Province.
  • C. Tolo
    Tolo is a coastal village and popular tourist resort in the Argolis region of the Peloponnese in Greece, known for its beaches and proximity to historic sites like Nafplio.
  • D. Tolo
    Tolo is an alternative name for the Talise language, an Austronesian language spoken in the Solomon Islands.
  • E. Tamasopo
    Tamasopo is a small town in the Huasteca Potosina region of San Luis Potosí, Mexico, known for its lush landscapes and popular nearby waterfalls and natural swimming areas.
  • 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: Tashman
Triple: [Lilyan Tashman, familyName, Tashman]
Generated description
Tashman is a surname most notably associated with American stage and film actress Lilyan Tashman, a prominent figure of early Hollywood cinema.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tashman
Target entity description: Tashman is a surname most notably associated with American stage and film actress Lilyan Tashman, a prominent figure of early Hollywood cinema.
  • A. Kiali
    Kiali is an observability and management console for Istio-based service meshes, providing traffic visualization, configuration insight, and troubleshooting tools.
  • B. Tashir
    Tashir is a small town located in Armenia’s northern Lori Province.
  • C. Tolo
    Tolo is a coastal village and popular tourist resort in the Argolis region of the Peloponnese in Greece, known for its beaches and proximity to historic sites like Nafplio.
  • D. Tolo
    Tolo is an alternative name for the Talise language, an Austronesian language spoken in the Solomon Islands.
  • E. Tamasopo
    Tamasopo is a small town in the Huasteca Potosina region of San Luis Potosí, Mexico, known for its lush landscapes and popular nearby waterfalls and natural swimming areas.
  • 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_69d85cd7683881908d405c1b5d7b4f7f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dedeea750c819082d8823c9ab6c5a2 completed April 15, 2026, 12:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5cb04e88190a42bb0e516df61bc completed May 9, 2026, 3:11 a.m.
NEDg Description generation batch_69fea66a04988190b483210c1671d287 completed May 9, 2026, 3:13 a.m.
NED2 Entity disambiguation (via description) batch_69fea70e2fbc81908f168925b06bdbd6 completed May 9, 2026, 3:16 a.m.
Created at: April 10, 2026, 3:02 a.m.