Triple

T979295
Position Surface form Disambiguated ID Type / Status
Subject Böblingen E21129 entity
Predicate hasTwinTown P919 FINISHED
Object Tsévié
Tsévié is a town in southern Togo that serves as an important regional center for trade and agriculture.
 E116440 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: Tsévié | Statement: [Böblingen, hasTwinTown, Tsévié]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tsévié
Context triple: [Böblingen, hasTwinTown, Tsévié]
  • A. Wanetsi
    Wanetsi is a distinct and archaic variety of Pashto spoken by a small community in parts of Afghanistan and Pakistan.
  • B. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • C. Samborondón
    Samborondón is a rapidly growing, affluent canton and town in coastal Ecuador, located across the river from Guayaquil and known for its upscale residential and commercial developments.
  • D. Tebu
    Tebu is a Saharan ethnic group and language community primarily inhabiting parts of southern Libya, Chad, and Niger.
  • E. Ndowe
    Ndowe is a Bantu language spoken by the Ndowe people along the coastal region of Equatorial Guinea.
  • 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: Tsévié
Triple: [Böblingen, hasTwinTown, Tsévié]
Generated description
Tsévié is a town in southern Togo that serves as an important regional center for trade and agriculture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tsévié
Target entity description: Tsévié is a town in southern Togo that serves as an important regional center for trade and agriculture.
  • A. Wanetsi
    Wanetsi is a distinct and archaic variety of Pashto spoken by a small community in parts of Afghanistan and Pakistan.
  • B. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • C. Samborondón
    Samborondón is a rapidly growing, affluent canton and town in coastal Ecuador, located across the river from Guayaquil and known for its upscale residential and commercial developments.
  • D. Tebu
    Tebu is a Saharan ethnic group and language community primarily inhabiting parts of southern Libya, Chad, and Niger.
  • E. Ndowe
    Ndowe is a Bantu language spoken by the Ndowe people along the coastal region of Equatorial Guinea.
  • 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_69a493c2b62c8190b616351789ec47f8 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b479e8f081908183448c36244e1f completed March 1, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac1cde59ac8190a04e3412805bc130 completed March 7, 2026, 12:41 p.m.
NEDg Description generation batch_69ac1d3b36c08190852dc68a1dc282f2 completed March 7, 2026, 12:42 p.m.
NED2 Entity disambiguation (via description) batch_69ac1ded444c81909a7b9f9e3869bd38 completed March 7, 2026, 12:45 p.m.
Created at: March 1, 2026, 7:40 p.m.