Triple

T3330097
Position Surface form Disambiguated ID Type / Status
Subject Nana E70011 entity
Predicate character P662 FINISHED
Object Louiset
Louiset is a fictional character associated with Nana, likely appearing in works or adaptations related to that name.
 E347893 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: Louiset | Statement: [Nana, character, Louiset]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Louiset
Context triple: [Nana, character, Louiset]
  • A. Breuillet
    Breuillet is a commune in the Essonne department in the Île-de-France region of northern France.
  • B. Lebrun
    Lebrun is a French surname borne by various notable figures in politics, arts, and other fields.
  • C. Montesson
    Montesson is a suburban commune in the Yvelines department of north-central France, located to the northwest of Paris along the Seine River.
  • D. Ganthier
    Ganthier is a commune in western Haiti known for its rural character and proximity to the capital, Port-au-Prince.
  • E. Lusser
    Lusser is a German surname most notably associated with engineer Robert Lusser, known for his contributions to aeronautics and reliability engineering.
  • 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: Louiset
Triple: [Nana, character, Louiset]
Generated description
Louiset is a fictional character associated with Nana, likely appearing in works or adaptations related to that name.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Louiset
Target entity description: Louiset is a fictional character associated with Nana, likely appearing in works or adaptations related to that name.
  • A. Breuillet
    Breuillet is a commune in the Essonne department in the Île-de-France region of northern France.
  • B. Lebrun
    Lebrun is a French surname borne by various notable figures in politics, arts, and other fields.
  • C. Montesson
    Montesson is a suburban commune in the Yvelines department of north-central France, located to the northwest of Paris along the Seine River.
  • D. Ganthier
    Ganthier is a commune in western Haiti known for its rural character and proximity to the capital, Port-au-Prince.
  • E. Lusser
    Lusser is a German surname most notably associated with engineer Robert Lusser, known for his contributions to aeronautics and reliability engineering.
  • 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_69ad85a24f208190bcf83131bfed3521 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb171ee0881908642504ab0ac8329 completed March 8, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b31a810e2c8190bfc206bdeb1ac5b8 completed March 12, 2026, 7:56 p.m.
NEDg Description generation batch_69b31c37f3a08190823c32e8f933ce82 completed March 12, 2026, 8:04 p.m.
NED2 Entity disambiguation (via description) batch_69b31caa4e188190b4dfd613fdaebdb6 completed March 12, 2026, 8:06 p.m.
Created at: March 8, 2026, 3:12 p.m.