Triple

T38063687

Position	Surface form	Disambiguated ID	Type / Status
Subject	Ma On Shan	`E950413`	entity
Predicate	hasTrailAccess	`P1017`	FINISHED
Object	Ma On Shan Country Park Ma On Shan Country Park is a protected natural area in Hong Kong known for its mountainous landscapes, hiking trails, and scenic views over the surrounding countryside and coastline.	`E2260285`	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: Ma On Shan Country Park | Statement: [Ma On Shan, hasTrailAccess, Ma On Shan Country 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: Ma On Shan Country Park
Triple: [Ma On Shan, hasTrailAccess, Ma On Shan Country Park]

Generated description

Ma On Shan Country Park is a protected natural area in Hong Kong known for its mountainous landscapes, hiking trails, and scenic views over the surrounding countryside and coastline.

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_69f76f01e63c819093b6012fc974f35a`	completed	May 3, 2026, 3:51 p.m.
NER	Named-entity recognition	`batch_69fbca364e74819087898e1aada08f15`	completed	May 6, 2026, 11:09 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a41852fef9c8190b353b4d36ef3f852`	completed	June 28, 2026, 8:33 p.m.
NEDg	Description generation	`batch_6a4185daa3388190aeb2b206279fb2b4`	completed	June 28, 2026, 8:36 p.m.
NED2	Entity disambiguation (via description)	`batch_6a41866fd40c8190a7533bb794d91bcd`	completed	June 28, 2026, 8:39 p.m.

Created at: May 3, 2026, 4:21 p.m.