Triple

T27870946
Position Surface form Disambiguated ID Type / Status
Subject Pollard script E704488 entity
Predicate alsoKnownAs P39 FINISHED
Object Missionary Miao script
Missionary Miao script is an alphabetic writing system devised in the early 20th century by missionary Samuel Pollard for transcribing the Miao (Hmong) language in China.
E1791321 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: Missionary Miao script | Statement: [Pollard script, alsoKnownAs, Missionary Miao script]
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: Missionary Miao script
Triple: [Pollard script, alsoKnownAs, Missionary Miao script]
Generated description
Missionary Miao script is an alphabetic writing system devised in the early 20th century by missionary Samuel Pollard for transcribing the Miao (Hmong) language in China.

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_69ef840f12408190b539d00d79658abf completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f6397b64f881909d811225e57aac5e completed May 2, 2026, 5:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12f74b120881909eae0b249e312224 completed May 24, 2026, 1:04 p.m.
NEDg Description generation batch_6a12f7d4807c8190a115da7557651b3d completed May 24, 2026, 1:06 p.m.
NED2 Entity disambiguation (via description) batch_6a12fb9bdbe881909c9f79d153f151a3 completed May 24, 2026, 1:22 p.m.
Created at: April 27, 2026, 6:24 p.m.