Triple

T27361809
Position Surface form Disambiguated ID Type / Status
Subject Christian Museum E685845 entity
Predicate near P350 FINISHED
Object Danube River at Esztergom
The Danube River at Esztergom is a scenic stretch of the Danube in northern Hungary, flowing past the historic town of Esztergom with its famous basilica and cultural landmarks.
 E1770928 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: Danube River at Esztergom | Statement: [Christian Museum, near, Danube River at Esztergom]
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: Danube River at Esztergom
Triple: [Christian Museum, near, Danube River at Esztergom]
Generated description
The Danube River at Esztergom is a scenic stretch of the Danube in northern Hungary, flowing past the historic town of Esztergom with its famous basilica and cultural 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_69ef14887c288190931b8431fdbf53c4 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f62c23b91c8190a430caf3791f932e completed May 2, 2026, 4:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7dd3de08190bafab71b2ea95e3a completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12a9ef43ac819097d5c47108692c15 completed May 24, 2026, 7:34 a.m.
NED2 Entity disambiguation (via description) batch_6a12ab2c6840819085f11be72866c959 completed May 24, 2026, 7:39 a.m.
Created at: April 27, 2026, 11:54 a.m.