Triple

T24910633
Position Surface form Disambiguated ID Type / Status
Subject Glamping Lakeside Rancabali E623834 entity
Predicate locatedNear P294 FINISHED
Object Situ Patenggang Lake
Situ Patenggang Lake is a scenic highland lake in the Rancabali area of Bandung, West Java, known for its cool climate, tea plantation surroundings, and popular boating and sightseeing spots.
E1661336 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: Situ Patenggang Lake | Statement: [Glamping Lakeside Rancabali, locatedNear, Situ Patenggang Lake]
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: Situ Patenggang Lake
Triple: [Glamping Lakeside Rancabali, locatedNear, Situ Patenggang Lake]
Generated description
Situ Patenggang Lake is a scenic highland lake in the Rancabali area of Bandung, West Java, known for its cool climate, tea plantation surroundings, and popular boating and sightseeing spots.

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_69e2fac889c081908e9ff686cb428e5a completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f4236e93c081908876aff0a06ed21a completed May 1, 2026, 3:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a104896c84c8190a3ab0c32b68532bc completed May 22, 2026, 12:14 p.m.
NEDg Description generation batch_6a1049b63de881908e04b30b555d7809 completed May 22, 2026, 12:19 p.m.
NED2 Entity disambiguation (via description) batch_6a104a868810819098fc6286e7599ea0 completed May 22, 2026, 12:22 p.m.
Created at: April 18, 2026, 5:27 a.m.