Triple

T27081661
Position Surface form Disambiguated ID Type / Status
Subject North Point E685610 entity
Predicate hasLandmark P105 FINISHED
Object Chun Yeung Street
Chun Yeung Street is a bustling traditional market street in Hong Kong’s North Point district, known for its wet market stalls, tram tracks running through the middle, and vibrant local atmosphere.
 E1759601 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: Chun Yeung Street | Statement: [North Point, hasLandmark, Chun Yeung Street]
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: Chun Yeung Street
Triple: [North Point, hasLandmark, Chun Yeung Street]
Generated description
Chun Yeung Street is a bustling traditional market street in Hong Kong’s North Point district, known for its wet market stalls, tram tracks running through the middle, and vibrant local atmosphere.

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_69ef14843b1481909d828b3d5a44550a completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f623417cfc81908943186b0b8c3e7b completed May 2, 2026, 4:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12536f7bbc8190b25a5b8eda928b66 completed May 24, 2026, 1:25 a.m.
NEDg Description generation batch_6a125433c0288190ab1e54c3d763468d completed May 24, 2026, 1:28 a.m.
NED2 Entity disambiguation (via description) batch_6a1254f46d288190aa6f45f8c8e9007d completed May 24, 2026, 1:31 a.m.
Created at: April 27, 2026, 8:35 a.m.