Triple

T3681375
Position Surface form Disambiguated ID Type / Status
Subject Broken Flowers E78118 entity
Predicate character P662 FINISHED
Object 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.
 E380794 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 | Statement: [Broken Flowers, character, Dora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dora
Context triple: [Broken Flowers, character, Dora]
  • A. Dora
    Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
  • B. Dora Riparia
    Dora Riparia is a river in northwestern Italy that flows through the city of Turin before joining the Po River.
  • C. Dora Luz
    Dora Luz was a Mexican singer and actress best known for her musical performances in classic Disney films of the 1940s.
  • D. Lucy
    Lucy, better known by her nickname Wyldstyle, is a rebellious and resourceful Master Builder from The Lego Movie franchise.
  • E. Lucy
    "Lucy" is a 2014 science fiction action film directed by Luc Besson, in which Scarlett Johansson plays a woman who gains extraordinary mental and physical abilities after a drug enters her system.
  • 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
Triple: [Broken Flowers, character, Dora]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dora
Target entity description: 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.
  • A. Dora
    Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
  • B. Dora Riparia
    Dora Riparia is a river in northwestern Italy that flows through the city of Turin before joining the Po River.
  • C. Dora Luz
    Dora Luz was a Mexican singer and actress best known for her musical performances in classic Disney films of the 1940s.
  • D. Lucy
    Lucy, better known by her nickname Wyldstyle, is a rebellious and resourceful Master Builder from The Lego Movie franchise.
  • E. Lucy
    "Lucy" is a 2014 science fiction action film directed by Luc Besson, in which Scarlett Johansson plays a woman who gains extraordinary mental and physical abilities after a drug enters her system.
  • 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_69ad85e18c1c8190be8aafb227f39f48 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc492aed481909e8986378ad283fc completed March 8, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4c3ae61908190beefd0df317b5eca completed March 14, 2026, 2:10 a.m.
NEDg Description generation batch_69b4c79cc8148190af8abfb393232a60 completed March 14, 2026, 2:27 a.m.
NED2 Entity disambiguation (via description) batch_69b4c94ccb5c819092d1ab8246e44108 completed March 14, 2026, 2:34 a.m.
Created at: March 8, 2026, 3:25 p.m.