Triple

T13227577
Position Surface form Disambiguated ID Type / Status
Subject Ladenburg E314920 entity
Predicate hasTwinTown P919 FINISHED
Object Garango
Garango is a town in Burkina Faso known for its cultural and municipal ties with the German town of Ladenburg.
E1041987 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: Garango | Statement: [Ladenburg, hasTwinTown, Garango]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Garango
Context triple: [Ladenburg, hasTwinTown, Garango]
  • A. Guraru
    Guraru is a town in the Indian state of Bihar, known as a local settlement within the Gaya region.
  • B. Bongwe
    Bongwe is a dialect of the Duala language spoken by the Duala people of Cameroon.
  • C. Ngala
    Ngala is a Bantu ethnic group and language community primarily found in the Democratic Republic of the Congo.
  • D. Ngala
    Ngala is a local government area in Borno State, northeastern Nigeria, located near the border with Cameroon and affected by regional security challenges.
  • E. Manyanga
    Manyanga was an important historical settlement in present-day Zimbabwe that served as the capital of the Rozvi Empire.
  • 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: Garango
Triple: [Ladenburg, hasTwinTown, Garango]
Generated description
Garango is a town in Burkina Faso known for its cultural and municipal ties with the German town of Ladenburg.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Garango
Target entity description: Garango is a town in Burkina Faso known for its cultural and municipal ties with the German town of Ladenburg.
  • A. Guraru
    Guraru is a town in the Indian state of Bihar, known as a local settlement within the Gaya region.
  • B. Bongwe
    Bongwe is a dialect of the Duala language spoken by the Duala people of Cameroon.
  • C. Ngala
    Ngala is a Bantu ethnic group and language community primarily found in the Democratic Republic of the Congo.
  • D. Ngala
    Ngala is a local government area in Borno State, northeastern Nigeria, located near the border with Cameroon and affected by regional security challenges.
  • E. Manyanga
    Manyanga was an important historical settlement in present-day Zimbabwe that served as the capital of the Rozvi Empire.
  • 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_69d806affc688190a25b6ccc588e9c72 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d3232d48190a3c792b025c596a6 completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7460e94a08190a518f466f55db482 completed May 3, 2026, 12:56 p.m.
NEDg Description generation batch_69f74b9dac6c8190b1fc3ed04fcf6d1f completed May 3, 2026, 1:20 p.m.
NED2 Entity disambiguation (via description) batch_69f74c5195188190bad111b301713426 completed May 3, 2026, 1:23 p.m.
Created at: April 9, 2026, 9:21 p.m.