Triple

T38572838
Position Surface form Disambiguated ID Type / Status
Subject historic center of Zaragoza E929322 entity
Predicate hasPart P35 FINISHED
Object Archbishop’s Palace of Zaragoza
The Archbishop’s Palace of Zaragoza is a historic ecclesiastical residence and architectural landmark located in the heart of Zaragoza, Spain.
E2279609 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: Archbishop’s Palace of Zaragoza | Statement: [historic center of Zaragoza, hasPart, Archbishop’s Palace of Zaragoza]
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: Archbishop’s Palace of Zaragoza
Triple: [historic center of Zaragoza, hasPart, Archbishop’s Palace of Zaragoza]
Generated description
The Archbishop’s Palace of Zaragoza is a historic ecclesiastical residence and architectural landmark located in the heart of Zaragoza, Spain.

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_69f76ebd2248819083978362d81fa35e completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fcd91eb5708190898c62af8c8201c1 completed May 7, 2026, 6:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41fd4ca3648190a4e1fb5434f194a8 completed June 29, 2026, 5:06 a.m.
NEDg Description generation batch_6a41fe94c1fc8190bb21fee5acc371ff completed June 29, 2026, 5:11 a.m.
NED2 Entity disambiguation (via description) batch_6a41ff1ca2d481909286a6c77f64b8b1 completed June 29, 2026, 5:14 a.m.
Created at: May 3, 2026, 4:32 p.m.