Triple

T30443624
Position Surface form Disambiguated ID Type / Status
Subject Champa–Vietnam relations E774518 entity
Predicate hasParticipant P149 FINISHED
Object Vijaya Champa
Vijaya Champa was a prominent historical Cham polity in what is now central Vietnam, known for its Hindu-Buddhist culture, maritime trade, and frequent conflicts with neighboring Vietnamese and Khmer states.
E1917853 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: Vijaya Champa | Statement: [Champa–Vietnam relations, hasParticipant, Vijaya Champa]
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: Vijaya Champa
Triple: [Champa–Vietnam relations, hasParticipant, Vijaya Champa]
Generated description
Vijaya Champa was a prominent historical Cham polity in what is now central Vietnam, known for its Hindu-Buddhist culture, maritime trade, and frequent conflicts with neighboring Vietnamese and Khmer states.

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_69f22493ef9c8190ae8c2afcb7f994c8 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6869c15d48190be8870c750df12ed completed May 2, 2026, 11:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27ac142e6c8190857258c93790984a completed June 9, 2026, 6 a.m.
NEDg Description generation batch_6a27b01d91c08190a94cca2e3e3c3bd5 completed June 9, 2026, 6:18 a.m.
NED2 Entity disambiguation (via description) batch_6a27b0d083a88190839b0c015391e69e completed June 9, 2026, 6:21 a.m.
Created at: April 29, 2026, 8:08 p.m.