Triple

T29146597
Position Surface form Disambiguated ID Type / Status
Subject Moura E738789 entity
Predicate hasLandmark P105 FINISHED
Object Igreja de São Pedro (Moura)
Igreja de São Pedro (Moura) is a historic Catholic church in the town of Moura, Portugal, noted for its religious significance and traditional Portuguese architectural features.
E1853382 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: Igreja de São Pedro (Moura) | Statement: [Moura, hasLandmark, Igreja de São Pedro (Moura)]
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: Igreja de São Pedro (Moura)
Triple: [Moura, hasLandmark, Igreja de São Pedro (Moura)]
Generated description
Igreja de São Pedro (Moura) is a historic Catholic church in the town of Moura, Portugal, noted for its religious significance and traditional Portuguese architectural features.

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_69f07cb46f148190874eb8576a447567 completed April 28, 2026, 9:24 a.m.
NER Named-entity recognition batch_69f66272f8548190b9274f67777b18b7 completed May 2, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25505ee5008190951480dcdcff9303 completed June 7, 2026, 11:05 a.m.
NEDg Description generation batch_6a25547c1cb881909b0a85b2bb6d61f1 completed June 7, 2026, 11:22 a.m.
NED2 Entity disambiguation (via description) batch_6a2558d26f808190b01d391c806b780d completed June 7, 2026, 11:41 a.m.
Created at: April 28, 2026, 11:40 a.m.