Triple

T28345720
Position Surface form Disambiguated ID Type / Status
Subject Póvoa de Lanhoso E717945 entity
Predicate hasLandmark P105 FINISHED
Object Castelo de Lanhoso
Castelo de Lanhoso is a historic medieval castle in northern Portugal, renowned for its hilltop fortress overlooking the town of Póvoa de Lanhoso.
 E1814860 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: Castelo de Lanhoso | Statement: [Póvoa de Lanhoso, hasLandmark, Castelo de Lanhoso]
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: Castelo de Lanhoso
Triple: [Póvoa de Lanhoso, hasLandmark, Castelo de Lanhoso]
Generated description
Castelo de Lanhoso is a historic medieval castle in northern Portugal, renowned for its hilltop fortress overlooking the town of Póvoa de Lanhoso.

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_69eff6eb30388190b898b96c4be6f49d completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64c063b448190a572f5a7049c4070 completed May 2, 2026, 7:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1627c2f0708190bd8977830faf4cc0 completed May 26, 2026, 11:07 p.m.
NEDg Description generation batch_6a162a20822c8190a158868dc54ac345 completed May 26, 2026, 11:17 p.m.
NED2 Entity disambiguation (via description) batch_6a162abe55f881909cef2ebc29c54074 completed May 26, 2026, 11:20 p.m.
Created at: April 28, 2026, 12:43 a.m.