Triple

T12556363
Position Surface form Disambiguated ID Type / Status
Subject Fernanda E295226 entity
Predicate hasShortForm P43 FINISHED
Object Nandinha
Nandinha is a diminutive, affectionate nickname commonly used in Portuguese for someone named Fernanda.
 E990809 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: Nandinha | Statement: [Fernanda, hasShortForm, Nandinha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nandinha
Context triple: [Fernanda, hasShortForm, Nandinha]
  • A. Nabaloi
    Nabaloi is an Austronesian language spoken by the Ibaloi people of the northern Philippines, particularly in the Benguet region of Luzon.
  • B. Amdanga
    Amdanga is a town in the North 24 Parganas district of the Indian state of West Bengal, known primarily as a semi-rural locality within the Kolkata metropolitan region.
  • C. Noukadubi
    Noukadubi is a 2011 Bengali-language film adaptation of Rabindranath Tagore’s novel of the same name, directed by Rituparno Ghosh.
  • D. Ráquira
    Ráquira is a Colombian town renowned for its traditional pottery, colorful handicrafts, and vibrant colonial architecture.
  • E. Ndar
    Ndar is the historical Wolof name for the city of Saint-Louis in Senegal, reflecting its pre-colonial and local cultural identity.
  • 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: Nandinha
Triple: [Fernanda, hasShortForm, Nandinha]
Generated description
Nandinha is a diminutive, affectionate nickname commonly used in Portuguese for someone named Fernanda.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nandinha
Target entity description: Nandinha is a diminutive, affectionate nickname commonly used in Portuguese for someone named Fernanda.
  • A. Nabaloi
    Nabaloi is an Austronesian language spoken by the Ibaloi people of the northern Philippines, particularly in the Benguet region of Luzon.
  • B. Amdanga
    Amdanga is a town in the North 24 Parganas district of the Indian state of West Bengal, known primarily as a semi-rural locality within the Kolkata metropolitan region.
  • C. Noukadubi
    Noukadubi is a 2011 Bengali-language film adaptation of Rabindranath Tagore’s novel of the same name, directed by Rituparno Ghosh.
  • D. Ráquira
    Ráquira is a Colombian town renowned for its traditional pottery, colorful handicrafts, and vibrant colonial architecture.
  • E. Ndar
    Ndar is the historical Wolof name for the city of Saint-Louis in Senegal, reflecting its pre-colonial and local cultural identity.
  • 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_69d6ad9cac2c81908e8a7bed82d1e21d completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d95490d2708190857f0cb9b8dd6a30 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f655895c9c819082f41e79906567c6 completed May 2, 2026, 7:50 p.m.
NEDg Description generation batch_69f6599e89708190bc1d38e9702d7626 completed May 2, 2026, 8:07 p.m.
NED2 Entity disambiguation (via description) batch_69f65a414118819095049600f1ad6d63 completed May 2, 2026, 8:10 p.m.
Created at: April 8, 2026, 11:47 p.m.