Triple

T7749042
Position Surface form Disambiguated ID Type / Status
Subject Province of Pisa E175706 entity
Predicate containsTown P847 FINISHED
Object Crespina Lorenzana
Crespina Lorenzana is a municipality in the Tuscany region of central Italy, known for its rural landscapes and traditional Tuscan character.
 E686279 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: Crespina Lorenzana | Statement: [Province of Pisa, containsTown, Crespina Lorenzana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Crespina Lorenzana
Context triple: [Province of Pisa, containsTown, Crespina Lorenzana]
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • C. Leonora
    Leonora is a feminine given name used in various cultures, often considered a variant of Eleanor or Leonore.
  • D. Leonora
    Leonora is a remote mining town in Western Australia’s Goldfields-Esperance region, historically significant for its goldfields and outback heritage.
  • E. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • 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: Crespina Lorenzana
Triple: [Province of Pisa, containsTown, Crespina Lorenzana]
Generated description
Crespina Lorenzana is a municipality in the Tuscany region of central Italy, known for its rural landscapes and traditional Tuscan character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Crespina Lorenzana
Target entity description: Crespina Lorenzana is a municipality in the Tuscany region of central Italy, known for its rural landscapes and traditional Tuscan character.
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • C. Leonora
    Leonora is a feminine given name used in various cultures, often considered a variant of Eleanor or Leonore.
  • D. Leonora
    Leonora is a remote mining town in Western Australia’s Goldfields-Esperance region, historically significant for its goldfields and outback heritage.
  • E. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • 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_69c69960b3588190a53aa590d31d9544 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c703affb6c8190adf4723dc1139edf completed March 27, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8be53b3788190a850ce1aaaac3aaa completed March 29, 2026, 5:53 a.m.
NEDg Description generation batch_69c8c235b1748190b6c17c5975e2eb9b completed March 29, 2026, 6:09 a.m.
NED2 Entity disambiguation (via description) batch_69c8c2f1dd508190853065d9e4e331b2 completed March 29, 2026, 6:13 a.m.
Created at: March 27, 2026, 4:08 p.m.