Triple

T10062961
Position Surface form Disambiguated ID Type / Status
Subject Province of Siena E213031 entity
Predicate contains P35 FINISHED
Object Murlo
Murlo is a small historic municipality in Tuscany, Italy, known for its Etruscan archaeological heritage and picturesque rural landscape.
 E839541 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: Murlo | Statement: [Province of Siena, contains, Murlo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Murlo
Context triple: [Province of Siena, contains, Murlo]
  • A. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • B. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • C. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • D. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • E. Marcali
    Marcali is a small town in southwestern Hungary known for its agricultural surroundings and role as a local administrative and service center in Somogy County.
  • 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: Murlo
Triple: [Province of Siena, contains, Murlo]
Generated description
Murlo is a small historic municipality in Tuscany, Italy, known for its Etruscan archaeological heritage and picturesque rural landscape.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Murlo
Target entity description: Murlo is a small historic municipality in Tuscany, Italy, known for its Etruscan archaeological heritage and picturesque rural landscape.
  • A. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • B. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • C. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • D. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • E. Marcali
    Marcali is a small town in southwestern Hungary known for its agricultural surroundings and role as a local administrative and service center in Somogy County.
  • 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_69ca83977128819084084eb7d1d8c52a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdcfd4e4ac8190a37061b4082caa48 completed April 2, 2026, 2:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29a7bd56c8190a6c43df26db880f4 completed April 5, 2026, 5:23 p.m.
NEDg Description generation batch_69d29b75634c819088c8ef750b1691d2 completed April 5, 2026, 5:27 p.m.
NED2 Entity disambiguation (via description) batch_69d29f5007f88190b0330d1a8c551905 completed April 5, 2026, 5:43 p.m.
Created at: March 30, 2026, 8:58 p.m.