Triple

T32272552
Position Surface form Disambiguated ID Type / Status
Subject Avenida de las Flores E824452 entity
Predicate nameMeaning P453 FINISHED
Object Avenue of the Flowers
Avenue of the Flowers is a picturesque, likely tree-lined or garden-adorned street or promenade known for its floral beauty and scenic charm.
 E2000377 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: Avenue of the Flowers | Statement: [Avenida de las Flores, nameMeaning, Avenue of the Flowers]
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: Avenue of the Flowers
Triple: [Avenida de las Flores, nameMeaning, Avenue of the Flowers]
Generated description
Avenue of the Flowers is a picturesque, likely tree-lined or garden-adorned street or promenade known for its floral beauty and scenic charm.

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_69f3490e73588190915f282edd105772 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bc89010c8190982399d265f171ac completed May 3, 2026, 3:10 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46e1eb6881909924a2593b233e5f completed June 15, 2026, 12:27 a.m.
NEDg Description generation batch_6a2f4abe9f9081908aae932b4496d1c7 completed June 15, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_6a2f4b5bfd4c819084a0fe18f7f38dd8 completed June 15, 2026, 12:46 a.m.
Created at: May 1, 2026, 12:42 a.m.