Triple

T24361474
Position Surface form Disambiguated ID Type / Status
Subject Prince Norodom Chakrapong E614070 entity
Predicate child P120 FINISHED
Object Norodom Rattana Devi
Norodom Rattana Devi is a Cambodian royal princess and politician, known for her involvement in national politics as a member of the Norodom family.
E1637252 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: Norodom Rattana Devi | Statement: [Prince Norodom Chakrapong, child, Norodom Rattana Devi]
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: Norodom Rattana Devi
Triple: [Prince Norodom Chakrapong, child, Norodom Rattana Devi]
Generated description
Norodom Rattana Devi is a Cambodian royal princess and politician, known for her involvement in national politics as a member of the Norodom family.

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_69e2d7dfe7f08190b7a1f3a36483ab05 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29384a2f88190885eb141c5c44a2d completed April 29, 2026, 11:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fee5ef08881908a6a8c2db80aed09 completed May 22, 2026, 5:49 a.m.
NEDg Description generation batch_6a0fef537a8c8190ac04651a1b03602b completed May 22, 2026, 5:53 a.m.
NED2 Entity disambiguation (via description) batch_6a0ff00803b481908e7315142e3eb396 completed May 22, 2026, 5:56 a.m.
Created at: April 18, 2026, 2 a.m.