Triple

T5551290
Position Surface form Disambiguated ID Type / Status
Subject Lorenzo E145530 entity
Predicate relatedName P3889 FINISHED
Object Loren
Loren is a given name used for people of any gender, often as a variant or shortened form of names like Lorenzo or Lauren.
 E532883 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: Loren | Statement: [Lorenzo, relatedName, Loren]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Loren
Context triple: [Lorenzo, relatedName, Loren]
  • A. Lorens
    Lorens is a character from Paulo Coelho’s novel "Brida," serving as one of the key figures in the protagonist’s spiritual and personal journey.
  • B. Lesnie
    Lesnie is the surname of Andrew Lesnie, the Academy Award–winning Australian cinematographer best known for his work on The Lord of the Rings film trilogy.
  • C. Kaven
    Kaven is one of the islands that make up Maloelap Atoll in the Marshall Islands, a Pacific island nation.
  • D. Arvin
    Arvin is a small agricultural city in Southern California’s San Joaquin Valley, known for its farming economy and diverse rural community.
  • E. Hayden
    Hayden is a surname most notably associated with American actor and author Sterling Hayden, known for his roles in classic mid-20th-century films.
  • 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: Loren
Triple: [Lorenzo, relatedName, Loren]
Generated description
Loren is a given name used for people of any gender, often as a variant or shortened form of names like Lorenzo or Lauren.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Loren
Target entity description: Loren is a given name used for people of any gender, often as a variant or shortened form of names like Lorenzo or Lauren.
  • A. Lorens
    Lorens is a character from Paulo Coelho’s novel "Brida," serving as one of the key figures in the protagonist’s spiritual and personal journey.
  • B. Lesnie
    Lesnie is the surname of Andrew Lesnie, the Academy Award–winning Australian cinematographer best known for his work on The Lord of the Rings film trilogy.
  • C. Kaven
    Kaven is one of the islands that make up Maloelap Atoll in the Marshall Islands, a Pacific island nation.
  • D. Arvin
    Arvin is a small agricultural city in Southern California’s San Joaquin Valley, known for its farming economy and diverse rural community.
  • E. Hayden
    Hayden is a surname most notably associated with American actor and author Sterling Hayden, known for his roles in classic mid-20th-century films.
  • 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_69c008fb879c81909f5bfa56fadc1d46 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01fe3e7788190aa5361b083197c17 completed March 22, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c028350bc08190a8b48893157b86a1 completed March 22, 2026, 5:34 p.m.
NEDg Description generation batch_69c037b4e04881908d07e704f2a161bb completed March 22, 2026, 6:40 p.m.
NED2 Entity disambiguation (via description) batch_69c0384f8ce481908e7f82edf6c17323 completed March 22, 2026, 6:43 p.m.
Created at: March 22, 2026, 3:35 p.m.