Triple

T34500479
Position Surface form Disambiguated ID Type / Status
Subject Jesse James Meets Frankenstein's Daughter E885735 entity
Predicate hasCharacter P2308 FINISHED
Object Juanita Lopez
Juanita Lopez is a character from the 1966 horror–Western film "Jesse James Meets Frankenstein's Daughter," which blends classic monster-movie elements with an Old West setting.
E2288590 NE FINISHED

How this triple was built (2 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: Juanita Lopez | Statement: [Jesse James Meets Frankenstein's Daughter, hasCharacter, Juanita Lopez]
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: Juanita Lopez
Triple: [Jesse James Meets Frankenstein's Daughter, hasCharacter, Juanita Lopez]
Generated description
Juanita Lopez is a character from the 1966 horror–Western film "Jesse James Meets Frankenstein's Daughter," which blends classic monster-movie elements with an Old West setting.

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_69f349cc0220819081f154c6964f4dc2 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f71f50f7dc8190bccff0a2fe80da6e completed May 3, 2026, 10:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5a9ff8c4b88190b4ef027b14031cb4 completed July 17, 2026, 9:34 p.m.
NEDg Description generation batch_6a5aa17461c08190ade5eea60a020a6f completed July 17, 2026, 9:41 p.m.
NED2 Entity disambiguation (via description) batch_6a5aa1c678b88190ac4ab943095419bb completed July 17, 2026, 9:42 p.m.
Created at: May 1, 2026, 2:01 a.m.