Triple

T24401872
Position Surface form Disambiguated ID Type / Status
Subject Royal Governor of La Florida E615193 entity
Predicate positionHeldBy P8 FINISHED
Object Gonzalo Méndez de Canço
Gonzalo Méndez de Canço was a Spanish colonial administrator and military officer who served as governor of Spanish Florida at the turn of the 17th century.
E1712933 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: Gonzalo Méndez de Canço | Statement: [Royal Governor of La Florida, positionHeldBy, Gonzalo Méndez de Canço]
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: Gonzalo Méndez de Canço
Triple: [Royal Governor of La Florida, positionHeldBy, Gonzalo Méndez de Canço]
Generated description
Gonzalo Méndez de Canço was a Spanish colonial administrator and military officer who served as governor of Spanish Florida at the turn of the 17th century.

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_69e2d7e780bc81908049c779e697a7f6 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f294db57248190b6f836269248b781 completed April 29, 2026, 11:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1185381e188190b7aec53f7381d7a7 completed May 23, 2026, 10:45 a.m.
NEDg Description generation batch_6a1185e028488190b74f377270fe1cdd completed May 23, 2026, 10:48 a.m.
NED2 Entity disambiguation (via description) batch_6a1186a40a3881908930fc8e7c8b9b63 completed May 23, 2026, 10:51 a.m.
Created at: April 18, 2026, 2:05 a.m.