Triple

T8492251
Position Surface form Disambiguated ID Type / Status
Subject Toran Darell E201000 entity
Predicate hasGivenName P17 FINISHED
Object Toran
Toran is a given name that can be used for individuals in various cultures and contexts.
 E737133 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: Toran | Statement: [Toran Darell, hasGivenName, Toran]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Toran
Context triple: [Toran Darell, hasGivenName, Toran]
  • A. Taninges
    Taninges is a commune in the Haute-Savoie department of southeastern France, situated in the French Alps.
  • B. Kalabar
    Kalabar is the primary villain and dark warlock in Disney's "Halloweentown," who seeks to conquer both the magical realm and the human world.
  • C. Tokoro
    Tokoro is a coastal district of Kitami City in Hokkaido, Japan, known historically for its fishing industry and drift ice along the Sea of Okhotsk.
  • D. Toramana
    Toramana was a prominent Huna ruler in early 6th-century northern India, known for his extensive military campaigns and significant role in weakening the Gupta Empire.
  • E. Tenjo
    Tenjo is a small municipality and town in the department of Cundinamarca, Colombia, known for its rural landscapes and proximity to Bogotá.
  • 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: Toran
Triple: [Toran Darell, hasGivenName, Toran]
Generated description
Toran is a given name that can be used for individuals in various cultures and contexts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Toran
Target entity description: Toran is a given name that can be used for individuals in various cultures and contexts.
  • A. Taninges
    Taninges is a commune in the Haute-Savoie department of southeastern France, situated in the French Alps.
  • B. Kalabar
    Kalabar is the primary villain and dark warlock in Disney's "Halloweentown," who seeks to conquer both the magical realm and the human world.
  • C. Tokoro
    Tokoro is a coastal district of Kitami City in Hokkaido, Japan, known historically for its fishing industry and drift ice along the Sea of Okhotsk.
  • D. Toramana
    Toramana was a prominent Huna ruler in early 6th-century northern India, known for his extensive military campaigns and significant role in weakening the Gupta Empire.
  • E. Tenjo
    Tenjo is a district in West Java, Indonesia, known as part of the greater Bogor area on the outskirts of Jakarta.
  • 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_69ca831ee390819095fae73400bbfafc completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe55cf5dc81908cad31ac53e15b46 completed March 31, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce3a5c260c8190bc7012a04363d260 completed April 2, 2026, 9:43 a.m.
NEDg Description generation batch_69ce3ca3be5c8190844e54805e9acaeb completed April 2, 2026, 9:53 a.m.
NED2 Entity disambiguation (via description) batch_69ce3d4e92e88190a90ba1567c569b00 completed April 2, 2026, 9:56 a.m.
Created at: March 30, 2026, 6:13 p.m.