Triple

T29413989
Position Surface form Disambiguated ID Type / Status
Subject Convento de la Encarnación, Madrid E745972 entity
Predicate locatedIn P40 FINISHED
Object Plaza de la Encarnación
Plaza de la Encarnación is a historic square in central Madrid known for its proximity to the Convento de la Encarnación and other notable landmarks.
E1921700 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: Plaza de la Encarnación | Statement: [Convento de la Encarnación, Madrid, locatedIn, Plaza de la Encarnación]
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: Plaza de la Encarnación
Triple: [Convento de la Encarnación, Madrid, locatedIn, Plaza de la Encarnación]
Generated description
Plaza de la Encarnación is a historic square in central Madrid known for its proximity to the Convento de la Encarnación and other notable landmarks.

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_69f0a79f6d5c8190a350baed0157e06f completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f66a3b1e108190bec0049dc39f8926 completed May 2, 2026, 9:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2856d1589081909f1fba2b402c33c4 completed June 9, 2026, 6:09 p.m.
NEDg Description generation batch_6a285983b18881909f6391f7e5dd45dd completed June 9, 2026, 6:20 p.m.
NED2 Entity disambiguation (via description) batch_6a285a1337088190a2fc8ab05a29fc2f completed June 9, 2026, 6:23 p.m.
Created at: April 28, 2026, 2:59 p.m.