Triple

T25176231
Position Surface form Disambiguated ID Type / Status
Subject Tulio Halperín Donghi E630453 entity
Predicate familyName P18 FINISHED
Object Halperín Donghi
Halperín Donghi is the surname of Tulio Halperín Donghi, a prominent Argentine historian known for his influential works on Latin American and Argentine political and intellectual history.
 E1665857 NE FINISHED

How this triple was built (2 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: Halperín Donghi | Statement: [Tulio Halperín Donghi, familyName, Halperín Donghi]
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: Halperín Donghi
Triple: [Tulio Halperín Donghi, familyName, Halperín Donghi]
Generated description
Halperín Donghi is the surname of Tulio Halperín Donghi, a prominent Argentine historian known for his influential works on Latin American and Argentine political and intellectual history.

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_69e75a88fdf081908e47ae6e195c14e1 completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f46dc040088190958bb7a6633fd016 completed May 1, 2026, 9:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105d1a18748190965bff0f8bf09987 completed May 22, 2026, 1:41 p.m.
NEDg Description generation batch_6a105dd1e43c8190b10f80fbfa0d1b87 completed May 22, 2026, 1:44 p.m.
NED2 Entity disambiguation (via description) batch_6a105e720558819080a749cd92cd6fc9 completed May 22, 2026, 1:47 p.m.
Created at: April 21, 2026, 12:34 p.m.