Triple

T24859245
Position Surface form Disambiguated ID Type / Status
Subject Hotel Formentor (historical) E622108 entity
Predicate foundedBy P104 FINISHED
Object Adán Diehl
Adán Diehl was an Argentine entrepreneur and cultural patron best known for creating the luxury Hotel Formentor in Mallorca, which became a renowned retreat for artists, writers, and intellectuals in the early 20th century.
E1710311 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: Adán Diehl | Statement: [Hotel Formentor (historical), foundedBy, Adán Diehl]
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: Adán Diehl
Triple: [Hotel Formentor (historical), foundedBy, Adán Diehl]
Generated description
Adán Diehl was an Argentine entrepreneur and cultural patron best known for creating the luxury Hotel Formentor in Mallorca, which became a renowned retreat for artists, writers, and intellectuals in the early 20th 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_69e2fac350d08190b3affde1b451a8c5 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f422eae9588190b3b9c8d4710c9bd4 completed May 1, 2026, 3:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1127185bb4819088a46534740ed77f completed May 23, 2026, 4:03 a.m.
NEDg Description generation batch_6a1134849eac8190a0f80898df1ae20c completed May 23, 2026, 5 a.m.
NED2 Entity disambiguation (via description) batch_6a11350decb88190b61a8491db612650 completed May 23, 2026, 5:03 a.m.
Created at: April 18, 2026, 5:21 a.m.