Triple

T29687733
Position Surface form Disambiguated ID Type / Status
Subject Cirebon Sultanate E751132 entity
Predicate hasRuler P5424 FINISHED
Object Sultan Kanoman
Sultan Kanoman is a ruler associated with the Kanoman branch of the Cirebon Sultanate, a historic Islamic monarchy on the north coast of Java in present-day Indonesia.
E1882263 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: Sultan Kanoman | Statement: [Cirebon Sultanate, hasRuler, Sultan Kanoman]
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: Sultan Kanoman
Triple: [Cirebon Sultanate, hasRuler, Sultan Kanoman]
Generated description
Sultan Kanoman is a ruler associated with the Kanoman branch of the Cirebon Sultanate, a historic Islamic monarchy on the north coast of Java in present-day Indonesia.

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_69f0d625b09481909b0b69aea1e846c8 completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f6729193908190b7a3e27a13fca854 completed May 2, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26aa6e304481909c87d54b0eab2ce7 completed June 8, 2026, 11:41 a.m.
NEDg Description generation batch_6a26b573e54881908b4605220884aac0 completed June 8, 2026, 12:28 p.m.
NED2 Entity disambiguation (via description) batch_6a26b942692081909ee799b9bca81112 completed June 8, 2026, 12:44 p.m.
Created at: April 28, 2026, 7:14 p.m.