Triple

T2497902
Position Surface form Disambiguated ID Type / Status
Subject ESV Thinline Bible E52394 entity
Predicate format P130 FINISHED
Object Thinline
Thinline is a slim, portable book format commonly used for Bibles that reduces bulk while retaining the full text.
 E271726 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: Thinline | Statement: [ESV Thinline Bible, format, Thinline]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Thinline
Context triple: [ESV Thinline Bible, format, Thinline]
  • A. Tiges
    Tiges is a popular nickname for the Richmond Football Club, an Australian rules football team in the AFL.
  • B. Brinkman
    Brinkman is a surname of Germanic origin borne by various notable individuals across fields such as sports, politics, and the arts.
  • C. Alvarez
    Alvarez is a common Spanish surname borne by numerous notable figures across fields such as science, sports, and the arts.
  • D. Espee
    Espee is a common nickname for the historic Southern Pacific Railroad, a major American railroad that operated in the western United States.
  • E. Olite
    Olite is a historic town in northern Spain renowned for its well-preserved medieval architecture and the royal palace of the kings of Navarre.
  • 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: Thinline
Triple: [ESV Thinline Bible, format, Thinline]
Generated description
Thinline is a slim, portable book format commonly used for Bibles that reduces bulk while retaining the full text.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Thinline
Target entity description: Thinline is a slim, portable book format commonly used for Bibles that reduces bulk while retaining the full text.
  • A. Tiges
    Tiges is a popular nickname for the Richmond Football Club, an Australian rules football team in the AFL.
  • B. Brinkman
    Brinkman is a surname of Germanic origin borne by various notable individuals across fields such as sports, politics, and the arts.
  • C. Alvarez
    Alvarez is a common Spanish surname borne by numerous notable figures across fields such as science, sports, and the arts.
  • D. Espee
    Espee is a common nickname for the historic Southern Pacific Railroad, a major American railroad that operated in the western United States.
  • E. Olite
    Olite is a historic town in northern Spain renowned for its well-preserved medieval architecture and the royal palace of the kings of Navarre.
  • 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_69ab4957b3a88190adf968ae0c1b931c completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd1ad2f8c81908853e97d75081e84 completed March 7, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f9be594819099a03a2784691124 completed March 9, 2026, 7:29 p.m.
NEDg Description generation batch_69af200e2db4819085851a45213edc89 completed March 9, 2026, 7:31 p.m.
NED2 Entity disambiguation (via description) batch_69af208dfab081909d706aad8ff5f615 completed March 9, 2026, 7:33 p.m.
Created at: March 6, 2026, 9:46 p.m.