Triple

T12042114
Position Surface form Disambiguated ID Type / Status
Subject Plzeň-City District E286687 entity
Predicate contains P35 FINISHED
Object Losiná
Losiná is a small municipality and village located in the Plzeň Region of the Czech Republic.
E961510 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: Losiná | Statement: [Plzeň-City District, contains, Losiná]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Losiná
Context triple: [Plzeň-City District, contains, Losiná]
  • A. Lanuza
    Lanuza is a coastal municipality in the Philippine province of Surigao del Sur known for its surfing spots and natural scenery.
  • B. Loiceño
    Loiceño is the Spanish demonym for a person from the municipality of Loíza in Puerto Rico.
  • C. Lopevi
    Lopevi is an Oceanic language of Vanuatu, traditionally spoken on Lopevi Island in the central part of the archipelago.
  • D. Libo
    Libo is an ancient Roman cognomen associated with members of the patrician Julii family.
  • E. Laja
    Laja is a small Chilean city in the Biobío Region, known for its riverside setting and proximity to the Biobío River.
  • 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: Losiná
Triple: [Plzeň-City District, contains, Losiná]
Generated description
Losiná is a small municipality and village located in the Plzeň Region of the Czech Republic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Losiná
Target entity description: Losiná is a small municipality and village located in the Plzeň Region of the Czech Republic.
  • A. Lanuza
    Lanuza is a coastal municipality in the Philippine province of Surigao del Sur known for its surfing spots and natural scenery.
  • B. Loiceño
    Loiceño is the Spanish demonym for a person from the municipality of Loíza in Puerto Rico.
  • C. Lopevi
    Lopevi is an Oceanic language of Vanuatu, traditionally spoken on Lopevi Island in the central part of the archipelago.
  • D. Libo
    Libo is an ancient Roman cognomen associated with members of the patrician Julii family.
  • E. Laja
    Laja is a small Chilean city in the Biobío Region, known for its riverside setting and proximity to the Biobío River.
  • 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_69d6ab4780948190bdb9f7620c2ac27e completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9040d13108190bd1a969fa62aae5a completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f49da728ec819080c349fd8d0ed62c completed May 1, 2026, 12:33 p.m.
NEDg Description generation batch_69f53d9460bc8190869f2b7d095d98cb completed May 1, 2026, 11:56 p.m.
NED2 Entity disambiguation (via description) batch_69f564b826ec819098906cf735e45093 completed May 2, 2026, 2:43 a.m.
Created at: April 8, 2026, 9:47 p.m.