Triple

T30150869
Position Surface form Disambiguated ID Type / Status
Subject North Bund E766390 entity
Predicate hasLandmark P105 FINISHED
Object North Bund Riverside Green Space
North Bund Riverside Green Space is a scenic waterfront park in Shanghai’s North Bund area, offering landscaped promenades, river views, and recreational spaces along the Huangpu River.
 E1901055 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: North Bund Riverside Green Space | Statement: [North Bund, hasLandmark, North Bund Riverside Green Space]
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: North Bund Riverside Green Space
Triple: [North Bund, hasLandmark, North Bund Riverside Green Space]
Generated description
North Bund Riverside Green Space is a scenic waterfront park in Shanghai’s North Bund area, offering landscaped promenades, river views, and recreational spaces along the Huangpu River.

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_69f22479cd088190ab4c6f3fce39d1c5 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67ed4297c81909062f19b7795f181 completed May 2, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a274cbe754881908b6dcfc7314ce886 completed June 8, 2026, 11:14 p.m.
NEDg Description generation batch_6a274d8dff508190b211a92328716611 completed June 8, 2026, 11:17 p.m.
NED2 Entity disambiguation (via description) batch_6a274e7037d48190869592da30780fc0 completed June 8, 2026, 11:21 p.m.
Created at: April 29, 2026, 7:20 p.m.