Triple

T3860021
Position Surface form Disambiguated ID Type / Status
Subject Cannery Row E90111 entity
Predicate mainCharacter P1183 FINISHED
Object Dora Flood
Dora Flood is a prominent madam and compassionate businesswoman who runs a bordello in John Steinbeck’s novel "Cannery Row."
 E394536 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: Dora Flood | Statement: [Cannery Row, mainCharacter, Dora Flood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dora Flood
Context triple: [Cannery Row, mainCharacter, Dora Flood]
  • A. Dora Luz
    Dora Luz was a Mexican singer and actress best known for her musical performances in classic Disney films of the 1940s.
  • B. Dora
    Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
  • C. Dora
    Dora is a character in Jim Jarmusch’s film "Broken Flowers," known as one of Don Johnston’s former girlfriends whom he visits while searching for the mother of his alleged son.
  • D. Dora Riparia
    Dora Riparia is a river in northwestern Italy that flows through the city of Turin before joining the Po River.
  • E. Dorys Madden
    Dorys Madden is best known as the wife of Basketball Hall of Famer Julius "Dr. J" Erving.
  • 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: Dora Flood
Triple: [Cannery Row, mainCharacter, Dora Flood]
Generated description
Dora Flood is a prominent madam and compassionate businesswoman who runs a bordello in John Steinbeck’s novel "Cannery Row."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dora Flood
Target entity description: Dora Flood is a prominent madam and compassionate businesswoman who runs a bordello in John Steinbeck’s novel "Cannery Row."
  • A. Dora Luz
    Dora Luz was a Mexican singer and actress best known for her musical performances in classic Disney films of the 1940s.
  • B. Dora
    Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
  • C. Dora
    Dora is a character in Jim Jarmusch’s film "Broken Flowers," known as one of Don Johnston’s former girlfriends whom he visits while searching for the mother of his alleged son.
  • D. Dora Riparia
    Dora Riparia is a river in northwestern Italy that flows through the city of Turin before joining the Po River.
  • E. Dorys Madden
    Dorys Madden is best known as the wife of Basketball Hall of Famer Julius "Dr. J" Erving.
  • 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_69aed95b3c088190a8f85d19e6070599 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec1ff39c8190b83a88abd840a0e3 completed March 9, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b512348fe88190b5ae942809732b76 completed March 14, 2026, 7:45 a.m.
NEDg Description generation batch_69b513013db481908f8fb5f56470c0d0 completed March 14, 2026, 7:49 a.m.
NED2 Entity disambiguation (via description) batch_69b5137200a08190bd2a78398e03803e completed March 14, 2026, 7:51 a.m.
Created at: March 9, 2026, 3:19 p.m.