Triple

T32594021
Position Surface form Disambiguated ID Type / Status
Subject Trần Phế Đế E833148 entity
Predicate mother P120 FINISHED
Object Empress Gia Từ
Empress Gia Từ was a royal consort of Vietnam’s Trần dynasty, best known as the mother of Emperor Trần Phế Đế.
 E2024695 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: Empress Gia Từ | Statement: [Trần Phế Đế, mother, Empress Gia Từ]
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: Empress Gia Từ
Triple: [Trần Phế Đế, mother, Empress Gia Từ]
Generated description
Empress Gia Từ was a royal consort of Vietnam’s Trần dynasty, best known as the mother of Emperor Trần Phế Đế.

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_69f34929ff648190aded9424aa7564ae completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c693fbfc8190ac7a90914510e2d7 completed May 3, 2026, 3:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34bcdaa8308190a5b19dff6cd308a4 completed June 19, 2026, 3:51 a.m.
NEDg Description generation batch_6a34bd4d9fe0819085c945c2d43eadef completed June 19, 2026, 3:53 a.m.
NED2 Entity disambiguation (via description) batch_6a34bdaafe788190a5d2ae0269802aa5 completed June 19, 2026, 3:55 a.m.
Created at: May 1, 2026, 1:05 a.m.