Triple

T31281771
Position Surface form Disambiguated ID Type / Status
Subject Zaryadye area E797688 entity
Predicate hasLandmark P105 FINISHED
Object Media Center in Zaryadye Park
The Media Center in Zaryadye Park is a modern cultural and exhibition complex in central Moscow that hosts multimedia installations, educational events, and interactive displays about the city and the park.
E797688 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: Media Center in Zaryadye Park | Statement: [Zaryadye area, hasLandmark, Media Center in Zaryadye Park]
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: Media Center in Zaryadye Park
Triple: [Zaryadye area, hasLandmark, Media Center in Zaryadye Park]
Generated description
The Media Center in Zaryadye Park is a modern cultural and exhibition complex in central Moscow that hosts multimedia installations, educational events, and interactive displays about the city and the park.

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_69f224def9088190a37034eab3daf57f completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69e00bbe88190b8e807d2a9522bc2 completed May 3, 2026, 12:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2a1e29f4108190bc7b2b6546f43195 completed June 11, 2026, 2:32 a.m.
NEDg Description generation batch_6a2a4c85e6388190b7d50eb6acfb5eda completed June 11, 2026, 5:49 a.m.
NED2 Entity disambiguation (via description) batch_6a2a4d32bafc8190bcb387f915413c6d completed June 11, 2026, 5:52 a.m.
Created at: April 29, 2026, 9:13 p.m.