Triple

T4527194
Position Surface form Disambiguated ID Type / Status
Subject Logudoro E106207 entity
Predicate contains P35 FINISHED
Object Tula
Tula is a town in the Logudoro region of northern Sardinia, Italy, known for its rural landscape and traditional Sardinian culture.
 E449800 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: Tula | Statement: [Logudoro, contains, Tula]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tula
Context triple: [Logudoro, contains, Tula]
  • A. Tula
    Tula is a historic Russian city south of Moscow, known for its metalworking, samovar production, and as a cultural center near Leo Tolstoy’s estate at Yasnaya Polyana.
  • B. Tula
    Tula is an important ancient Mesoamerican city, once a major Toltec capital known for its monumental architecture and iconic stone warrior statues.
  • C. Tula
    Tula is the birth name of American actress and dancer Cyd Charisse, famed for her roles in classic Hollywood musicals.
  • D. Tula
    Tula is a small coastal village in the Eastern District of American Samoa known for its traditional Samoan culture and scenic Pacific island setting.
  • E. Tenosique
    Tenosique is a municipality and city in southeastern Mexico known for its location in the state of Tabasco near the Guatemalan border and along key migration routes.
  • 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: Tula
Triple: [Logudoro, contains, Tula]
Generated description
Tula is a town in the Logudoro region of northern Sardinia, Italy, known for its rural landscape and traditional Sardinian culture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tula
Target entity description: Tula is a town in the Logudoro region of northern Sardinia, Italy, known for its rural landscape and traditional Sardinian culture.
  • A. Tula
    Tula is a historic Russian city south of Moscow, known for its metalworking, samovar production, and as a cultural center near Leo Tolstoy’s estate at Yasnaya Polyana.
  • B. Tula
    Tula is an important ancient Mesoamerican city, once a major Toltec capital known for its monumental architecture and iconic stone warrior statues.
  • C. Tula
    Tula is the birth name of American actress and dancer Cyd Charisse, famed for her roles in classic Hollywood musicals.
  • D. Tula
    Tula is a small coastal village in the Eastern District of American Samoa known for its traditional Samoan culture and scenic Pacific island setting.
  • E. Tenosique
    Tenosique is a municipality and city in southeastern Mexico known for its location in the state of Tabasco near the Guatemalan border and along key migration routes.
  • 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_69bd43f3d6e08190a91824f833d51bbe completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd577737848190ad509c8bb8e57ec0 completed March 20, 2026, 2:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69bda4577d0c8190a88cdf4523446329 completed March 20, 2026, 7:47 p.m.
NEDg Description generation batch_69bda8367b988190bd6859581ba9a38e completed March 20, 2026, 8:04 p.m.
NED2 Entity disambiguation (via description) batch_69bda8b4d064819083de58ee18458fa8 completed March 20, 2026, 8:06 p.m.
Created at: March 20, 2026, 1:03 p.m.