Triple

T25812115
Position Surface form Disambiguated ID Type / Status
Subject Umbul Binangun bathing pool E650138 entity
Predicate partOf P40 FINISHED
Object Taman Sari complex
The Taman Sari complex is a former royal garden and pleasure palace of the Yogyakarta Sultanate in Indonesia, known for its historic architecture, bathing pools, and water features.
E1702160 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: Taman Sari complex | Statement: [Umbul Binangun bathing pool, partOf, Taman Sari complex]
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: Taman Sari complex
Triple: [Umbul Binangun bathing pool, partOf, Taman Sari complex]
Generated description
The Taman Sari complex is a former royal garden and pleasure palace of the Yogyakarta Sultanate in Indonesia, known for its historic architecture, bathing pools, and water features.

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_69e7ab35d264819095367f7e80c983ff completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f600c5b8048190a116f2b0521e9814 completed May 2, 2026, 1:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10eca63c588190a76afa281a1cee55 completed May 22, 2026, 11:54 p.m.
NEDg Description generation batch_6a10eddf8e008190a604d8c0db0fdd9d completed May 22, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_6a10efe2fc188190ab9d5e8276a1ef2f completed May 23, 2026, 12:08 a.m.
Created at: April 22, 2026, 7:11 a.m.