Triple

T30764229
Position Surface form Disambiguated ID Type / Status
Subject Underground Tourist Route in Rzeszów E783319 entity
Predicate locatedUnder P10157 FINISHED
Object Old Town of Rzeszów
The Old Town of Rzeszów is the historic center of the city, characterized by its preserved medieval urban layout, market square, and notable architectural landmarks.
 E1932002 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: Old Town of Rzeszów | Statement: [Underground Tourist Route in Rzeszów, locatedUnder, Old Town of Rzeszów]
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: Old Town of Rzeszów
Triple: [Underground Tourist Route in Rzeszów, locatedUnder, Old Town of Rzeszów]
Generated description
The Old Town of Rzeszów is the historic center of the city, characterized by its preserved medieval urban layout, market square, and notable architectural 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_69f224b047f48190b4f5efeb7ee97b37 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f68fbafbc4819095a36c8ccf608452 completed May 2, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28b0963ff08190a8a01f8053df86ec completed June 10, 2026, 12:32 a.m.
NEDg Description generation batch_6a28b4e1fc608190b4382fa665dc6fed completed June 10, 2026, 12:50 a.m.
NED2 Entity disambiguation (via description) batch_6a28b61b4e148190a27ad4358b3e21cb completed June 10, 2026, 12:55 a.m.
Created at: April 29, 2026, 8:39 p.m.