Triple

T9269213
Position Surface form Disambiguated ID Type / Status
Subject Pero E222779 entity
Predicate hasAlternativeName P39 FINISHED
Object Pipero
Pipero is an alternative name for Pero, likely referring to the same individual or character known primarily as Pero.
 E788789 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: Pipero | Statement: [Pero, hasAlternativeName, Pipero]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pipero
Context triple: [Pero, hasAlternativeName, Pipero]
  • A. Pipera
    Pipera is a major Bucharest Metro terminus station serving the Pipera business and industrial district in northern Bucharest, Romania.
  • B. Alvito
    Alvito is a small Portuguese municipality in the Alentejo region, known for its historic castle and traditional rural landscape.
  • C. Pio
    Pio is the costumed mascot representing the athletic teams and school spirit of Lewis & Clark College.
  • D. Pometino
    Pometino is the Italian demonym for a resident or native of the town of Pomezia in the Lazio region.
  • E. Molinaro
    Molinaro is an Italian occupational surname, historically associated with millers and derived from the same root as "Molinero."
  • 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: Pipero
Triple: [Pero, hasAlternativeName, Pipero]
Generated description
Pipero is an alternative name for Pero, likely referring to the same individual or character known primarily as Pero.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pipero
Target entity description: Pipero is an alternative name for Pero, likely referring to the same individual or character known primarily as Pero.
  • A. Pipera
    Pipera is a major Bucharest Metro terminus station serving the Pipera business and industrial district in northern Bucharest, Romania.
  • B. Alvito
    Alvito is a small Portuguese municipality in the Alentejo region, known for its historic castle and traditional rural landscape.
  • C. Pio
    Pio is the costumed mascot representing the athletic teams and school spirit of Lewis & Clark College.
  • D. Pometino
    Pometino is the Italian demonym for a resident or native of the town of Pomezia in the Lazio region.
  • E. Molinaro
    Molinaro is an Italian occupational surname, historically associated with millers and derived from the same root as "Molinero."
  • 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_69ca841ffe208190aa7bcffbef2f8379 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd074ef7408190b213c09491918132 completed April 1, 2026, 11:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69d09c2239a08190b954c8c57ced8fd2 completed April 4, 2026, 5:05 a.m.
NEDg Description generation batch_69d09cf11e488190b61f4a61002454e6 completed April 4, 2026, 5:09 a.m.
NED2 Entity disambiguation (via description) batch_69d09e2450048190b5aa31507e54d6c8 completed April 4, 2026, 5:14 a.m.
Created at: March 30, 2026, 7:33 p.m.