Triple

T34536850
Position Surface form Disambiguated ID Type / Status
Subject Plaza Real E886691 entity
Predicate architect P184 FINISHED
Object Francesc Daniel Molina i Casamajó
Francesc Daniel Molina i Casamajó was a 19th-century Catalan architect known for his influential urban designs in Barcelona, including prominent public squares and civic spaces.
E2120686 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: Francesc Daniel Molina i Casamajó | Statement: [Plaza Real, architect, Francesc Daniel Molina i Casamajó]
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: Francesc Daniel Molina i Casamajó
Triple: [Plaza Real, architect, Francesc Daniel Molina i Casamajó]
Generated description
Francesc Daniel Molina i Casamajó was a 19th-century Catalan architect known for his influential urban designs in Barcelona, including prominent public squares and civic spaces.

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_69f349ce5eb881909e431c670944aa68 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f71feeedc081908468bf1e2e2ef3e0 completed May 3, 2026, 10:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a37b24f64d4819099e71100951651b7 completed June 21, 2026, 9:43 a.m.
NEDg Description generation batch_6a37b3b401f08190bab5b2b591ddf163 completed June 21, 2026, 9:49 a.m.
NED2 Entity disambiguation (via description) batch_6a37b5345c088190b28e008ba62610da completed June 21, 2026, 9:56 a.m.
Created at: May 1, 2026, 2:02 a.m.