Triple

T11093568
Position Surface form Disambiguated ID Type / Status
Subject Sara languages E262315 entity
Predicate hasMember P10 FINISHED
Object Fer language
Fer language is a Central Sudanic language spoken by the Fer people of the Central African Republic and surrounding regions.
E904335 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: Fer language | Statement: [Sara languages, hasMember, Fer language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fer language
Context triple: [Sara languages, hasMember, Fer language]
  • A. Fang language
    Fang is a Bantu language spoken primarily by the Fang people of Equatorial Guinea, Gabon, and Cameroon, notable for its significant influence on local varieties of Spanish and French.
  • B. Phong language
    The Phong language is a lesser-known Vietic language spoken by an ethnic minority community in Laos.
  • C. Mara language
    The Mara language is a Kuki-Chin language spoken primarily by the Mara people in parts of northeastern India and western Myanmar.
  • D. Pear language
    Pear language is an Austroasiatic language of the Pearic branch spoken by the Pear people of Cambodia and considered highly endangered.
  • E. Eiffel programming language
    Eiffel is an object-oriented programming language designed by Bertrand Meyer that emphasizes software correctness through features like Design by Contract and strong support for modular, reusable code.
  • 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: Fer language
Triple: [Sara languages, hasMember, Fer language]
Generated description
Fer language is a Central Sudanic language spoken by the Fer people of the Central African Republic and surrounding regions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Fer language
Target entity description: Fer language is a Central Sudanic language spoken by the Fer people of the Central African Republic and surrounding regions.
  • A. Fang language
    Fang is a Bantu language spoken primarily by the Fang people of Equatorial Guinea, Gabon, and Cameroon, notable for its significant influence on local varieties of Spanish and French.
  • B. Phong language
    The Phong language is a lesser-known Vietic language spoken by an ethnic minority community in Laos.
  • C. Mara language
    The Mara language is a Kuki-Chin language spoken primarily by the Mara people in parts of northeastern India and western Myanmar.
  • D. Pear language
    Pear language is an Austroasiatic language of the Pearic branch spoken by the Pear people of Cambodia and considered highly endangered.
  • E. Eiffel programming language
    Eiffel is an object-oriented programming language designed by Bertrand Meyer that emphasizes software correctness through features like Design by Contract and strong support for modular, reusable code.
  • 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_69d6aa9a40d88190a373e2c7e48285db completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d799ed12d88190a4ad8c346d68f11f completed April 9, 2026, 12:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3e7d3043c8190bdbe0ec51992db0c completed April 18, 2026, 8:21 p.m.
NEDg Description generation batch_69e3f2cbb4708190a328cff473104d14 completed April 18, 2026, 9:08 p.m.
NED2 Entity disambiguation (via description) batch_69e3f497a01881909d1dae70a02e5f97 completed April 18, 2026, 9:16 p.m.
Created at: April 8, 2026, 9:27 p.m.