Triple

T32451885
Position Surface form Disambiguated ID Type / Status
Subject Palazzo Vescovile (Teramo) E829311 entity
Predicate isInHistoricCenter P30451 FINISHED
Object Teramo historic center
Teramo historic center is the medieval and Renaissance core of the Italian city of Teramo, characterized by narrow streets, historic palaces, and notable religious buildings.
E2007860 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: Teramo historic center | Statement: [Palazzo Vescovile (Teramo), isInHistoricCenter, Teramo historic center]
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: Teramo historic center
Triple: [Palazzo Vescovile (Teramo), isInHistoricCenter, Teramo historic center]
Generated description
Teramo historic center is the medieval and Renaissance core of the Italian city of Teramo, characterized by narrow streets, historic palaces, and notable religious buildings.

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_69f3491df9288190afc0b23b1d6e72ce completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c31264b48190971164ebcfc5f459 completed May 3, 2026, 3:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34667a829481909a0dad637badbba0 completed June 18, 2026, 9:43 p.m.
NEDg Description generation batch_6a34674302f081908ce094e58ee8360c completed June 18, 2026, 9:46 p.m.
NED2 Entity disambiguation (via description) batch_6a3468279dbc8190b5efcecd6f4aa23c completed June 18, 2026, 9:50 p.m.
Created at: May 1, 2026, 12:56 a.m.