Triple

T25574995
Position Surface form Disambiguated ID Type / Status
Subject Francos Rodríguez E641080 entity
Predicate namedAfter P63 FINISHED
Object Calle de Francos Rodríguez
Calle de Francos Rodríguez is a street in Madrid, Spain, known for honoring the politician and engineer José Francos Rodríguez and connecting key neighborhoods in the city.
E1820738 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: Calle de Francos Rodríguez | Statement: [Francos Rodríguez, namedAfter, Calle de Francos Rodríguez]
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: Calle de Francos Rodríguez
Triple: [Francos Rodríguez, namedAfter, Calle de Francos Rodríguez]
Generated description
Calle de Francos Rodríguez is a street in Madrid, Spain, known for honoring the politician and engineer José Francos Rodríguez and connecting key neighborhoods in the city.

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_69e75dc281bc819095ec04dc0c3a94d0 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f92fb11c819086165e59ffef4910 completed May 2, 2026, 1:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a164151f6348190a83d4f06ed04ba38 completed May 27, 2026, 12:56 a.m.
NEDg Description generation batch_6a164228e3ac8190a1574562a734b13f completed May 27, 2026, 1 a.m.
NED2 Entity disambiguation (via description) batch_6a164611b60c819083a14fcba602a299 completed May 27, 2026, 1:17 a.m.
Created at: April 21, 2026, 4 p.m.