Triple

T9531768
Position Surface form Disambiguated ID Type / Status
Subject Danionidae E229912 entity
Predicate contains P35 FINISHED
Object Horadandia
Horadandia is a small genus of tiny freshwater cyprinid fishes native to South Asia, known for their miniature size and occurrence in shallow, vegetated waters.
 E805071 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: Horadandia | Statement: [Danionidae, contains, Horadandia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Horadandia
Context triple: [Danionidae, contains, Horadandia]
  • A. Dunedoo
    Dunedoo is a small rural town in New South Wales, Australia, known for its agricultural surroundings and role as a service centre for the surrounding farming district.
  • B. Palena
    Palena is a small town and municipality in the Palena Province of Chile’s Los Lagos Region, known for its remote Andean landscapes and outdoor tourism.
  • C. Daulian
    Daulian refers to an inhabitant or native of the ancient Greek town of Daulis in Phocis.
  • D. Kaledupa
    Kaledupa is an island in Indonesia’s Wakatobi archipelago, known for its traditional villages, mangrove forests, and rich surrounding coral reefs.
  • E. Mauregard
    Mauregard is a small commune in the Seine-et-Marne department of the Île-de-France region in north-central France, situated near Paris Charles de Gaulle Airport.
  • 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: Horadandia
Triple: [Danionidae, contains, Horadandia]
Generated description
Horadandia is a small genus of tiny freshwater cyprinid fishes native to South Asia, known for their miniature size and occurrence in shallow, vegetated waters.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Horadandia
Target entity description: Horadandia is a small genus of tiny freshwater cyprinid fishes native to South Asia, known for their miniature size and occurrence in shallow, vegetated waters.
  • A. Dunedoo
    Dunedoo is a small rural town in New South Wales, Australia, known for its agricultural surroundings and role as a service centre for the surrounding farming district.
  • B. Palena
    Palena is a small town and municipality in the Palena Province of Chile’s Los Lagos Region, known for its remote Andean landscapes and outdoor tourism.
  • C. Daulian
    Daulian refers to an inhabitant or native of the ancient Greek town of Daulis in Phocis.
  • D. Kaledupa
    Kaledupa is an island in Indonesia’s Wakatobi archipelago, known for its traditional villages, mangrove forests, and rich surrounding coral reefs.
  • E. Mauregard
    Mauregard is a small commune in the Seine-et-Marne department of the Île-de-France region in north-central France, situated near Paris Charles de Gaulle Airport.
  • 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_69ca8479934c81908006d0e6e970ae05 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98b408648190a04127c1d47fe7d2 completed April 1, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c38a3848190ab3561f70497c9eb completed April 4, 2026, 5:36 p.m.
NEDg Description generation batch_69d14d23573c8190aeebf2fdac20a332 completed April 4, 2026, 5:40 p.m.
NED2 Entity disambiguation (via description) batch_69d14da4514481908a530b5d77ad832a completed April 4, 2026, 5:43 p.m.
Created at: March 30, 2026, 8 p.m.