Triple

T23973952
Position Surface form Disambiguated ID Type / Status
Subject Ruiz-Tagle E604310 entity
Predicate hasNotableBearer P458 FINISHED
Object Francisco Ruiz-Tagle
Francisco Ruiz-Tagle was a Chilean lawyer and conservative politician who briefly served as President of Chile in 1830 during a turbulent period of the country’s early republican history.
E1619504 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: Francisco Ruiz-Tagle | Statement: [Ruiz-Tagle, hasNotableBearer, Francisco Ruiz-Tagle]
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: Francisco Ruiz-Tagle
Triple: [Ruiz-Tagle, hasNotableBearer, Francisco Ruiz-Tagle]
Generated description
Francisco Ruiz-Tagle was a Chilean lawyer and conservative politician who briefly served as President of Chile in 1830 during a turbulent period of the country’s early republican 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_69e29543019c8190872462e593cc50b4 completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f1d1dda91c8190af716bceb3225aee completed April 29, 2026, 9:39 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0facfbebec81909f2bbddeffbff6ea completed May 22, 2026, 1:10 a.m.
NEDg Description generation batch_6a0fad5e3ff0819080250111feb37ec2 completed May 22, 2026, 1:11 a.m.
NED2 Entity disambiguation (via description) batch_6a0fadf7bbd08190aa99cd9d0e53afa1 completed May 22, 2026, 1:14 a.m.
Created at: April 17, 2026, 9:26 p.m.