Triple

T201020
Position Surface form Disambiguated ID Type / Status
Subject Mickey Rooney E4503 entity
Predicate spouse P13 FINISHED
Object Marge Lane
Marge Lane was an American actress best known for being one of the early wives of film star Mickey Rooney.
E25765 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: Marge Lane | Statement: [Mickey Rooney, spouse, Marge Lane]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marge Lane
Context triple: [Mickey Rooney, spouse, Marge Lane]
  • A. Lane
    Lane is a common English surname borne by numerous notable individuals across fields such as science, politics, and the arts.
  • B. Lynn
    Lynn is a coastal city in northeastern Massachusetts, known as one of the larger urban centers in the Greater Boston metropolitan area.
  • C. Nichols Bridgeway
    Nichols Bridgeway is an elevated pedestrian bridge in Chicago that connects Millennium Park to the Art Institute of Chicago, offering views of the city skyline.
  • D. South Hall Road
    South Hall Road is a campus roadway or pedestrian route associated with the University of California, Berkeley, situated near the historic Sather Gate.
  • E. Nancy
    Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
  • 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: Marge Lane
Triple: [Mickey Rooney, spouse, Marge Lane]
Generated description
Marge Lane was an American actress best known for being one of the early wives of film star Mickey Rooney.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Marge Lane
Target entity description: Marge Lane was an American actress best known for being one of the early wives of film star Mickey Rooney.
  • A. Lane
    Lane is a common English surname borne by numerous notable individuals across fields such as science, politics, and the arts.
  • B. Lynn
    Lynn is a coastal city in northeastern Massachusetts, known as one of the larger urban centers in the Greater Boston metropolitan area.
  • C. Nichols Bridgeway
    Nichols Bridgeway is an elevated pedestrian bridge in Chicago that connects Millennium Park to the Art Institute of Chicago, offering views of the city skyline.
  • D. South Hall Road
    South Hall Road is a campus roadway or pedestrian route associated with the University of California, Berkeley, situated near the historic Sather Gate.
  • E. Nancy
    Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
  • 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_69a25737567c81908f9c505300239181 completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25be47ea881909c296b30a0d47a65 completed Feb. 28, 2026, 3:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3232f11a08190ad532c68d9e8e2da completed Feb. 28, 2026, 5:17 p.m.
NEDg Description generation batch_69a3244be2b48190a44c07f04ddc623d completed Feb. 28, 2026, 5:22 p.m.
NED2 Entity disambiguation (via description) batch_69a324b5516c8190bda5ecb3c4c0d370 completed Feb. 28, 2026, 5:24 p.m.
Created at: Feb. 28, 2026, 2:51 a.m.