Triple

T28348557
Position Surface form Disambiguated ID Type / Status
Subject Xinghai Square E718023 entity
Predicate hasNearbyAttraction P2064 FINISHED
Object Dalian Modern Museum
Dalian Modern Museum is a contemporary museum in Dalian, China, known for its modern architecture and exhibitions highlighting the city’s history, culture, and urban development.
 E1814949 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: Dalian Modern Museum | Statement: [Xinghai Square, hasNearbyAttraction, Dalian Modern Museum]
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: Dalian Modern Museum
Triple: [Xinghai Square, hasNearbyAttraction, Dalian Modern Museum]
Generated description
Dalian Modern Museum is a contemporary museum in Dalian, China, known for its modern architecture and exhibitions highlighting the city’s history, culture, and urban development.

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_69eff6ec27b481908c8d7b86c47893d9 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64c08f0148190a0a87da4d8fed07e completed May 2, 2026, 7:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1627c2f0708190bd8977830faf4cc0 completed May 26, 2026, 11:07 p.m.
NEDg Description generation batch_6a162a20822c8190a158868dc54ac345 completed May 26, 2026, 11:17 p.m.
NED2 Entity disambiguation (via description) batch_6a162abe55f881909cef2ebc29c54074 completed May 26, 2026, 11:20 p.m.
Created at: April 28, 2026, 12:44 a.m.