Triple

T26023513
Position Surface form Disambiguated ID Type / Status
Subject Ottorino Respighi E647217 entity
Predicate notableWork P4 FINISHED
Object La bella dormente nel bosco
La bella dormente nel bosco is a fairy-tale opera by Italian composer Ottorino Respighi, based on the Sleeping Beauty story and noted for its lyrical, evocative orchestration.
E1702978 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: La bella dormente nel bosco | Statement: [Ottorino Respighi, notableWork, La bella dormente nel bosco]
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: La bella dormente nel bosco
Triple: [Ottorino Respighi, notableWork, La bella dormente nel bosco]
Generated description
La bella dormente nel bosco is a fairy-tale opera by Italian composer Ottorino Respighi, based on the Sleeping Beauty story and noted for its lyrical, evocative orchestration.

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_69e77e8b60e88190a3b26c4f0032a2c2 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f605e8c0a08190a34cad51a19e92de completed May 2, 2026, 2:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1107aae5988190b1ac8478f90a66b6 completed May 23, 2026, 1:49 a.m.
NEDg Description generation batch_6a110851836c8190847505b2b32bb915 completed May 23, 2026, 1:52 a.m.
NED2 Entity disambiguation (via description) batch_6a1108d6dbfc8190b95ecc9466e182b9 completed May 23, 2026, 1:54 a.m.
Created at: April 22, 2026, 9:05 a.m.