Triple

T12653804
Position Surface form Disambiguated ID Type / Status
Subject Loma E302229 entity
Predicate alternativeName P39 FINISHED
Object Lorma
Lorma is an ethnic group primarily found in Liberia, known for its distinct language and cultural traditions.
 E996633 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: Lorma | Statement: [Loma, alternativeName, Lorma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lorma
Context triple: [Loma, alternativeName, Lorma]
  • A. 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.
  • B. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • C. Kumba
    Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
  • D. Kathumar
    Kathumar is a town in the Alwar district of Rajasthan, India, known as a local administrative and market center in the region.
  • E. Takelsa
    Takelsa is a coastal town in northeastern Tunisia known for its agriculture and location within the Nabeul region on the Cap Bon peninsula.
  • 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: Lorma
Triple: [Loma, alternativeName, Lorma]
Generated description
Lorma is an ethnic group primarily found in Liberia, known for its distinct language and cultural traditions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lorma
Target entity description: Lorma is an ethnic group primarily found in Liberia, known for its distinct language and cultural traditions.
  • A. 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.
  • B. Kumba
    Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
  • C. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • D. Kathumar
    Kathumar is a town in the Alwar district of Rajasthan, India, known as a local administrative and market center in the region.
  • E. Takelsa
    Takelsa is a coastal town in northeastern Tunisia known for its agriculture and location within the Nabeul region on the Cap Bon peninsula.
  • 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_69d7bded71a88190bb76e2413af9ea66 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96160730c81909e1aa3efb51bf159 completed April 10, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6688104d48190939933b93b7e60cc completed May 2, 2026, 9:11 p.m.
NEDg Description generation batch_69f66c572f848190a8cad6311d3315a3 completed May 2, 2026, 9:27 p.m.
NED2 Entity disambiguation (via description) batch_69f66cef79148190a052fb9ade3b0d27 completed May 2, 2026, 9:30 p.m.
Created at: April 9, 2026, 5:18 p.m.