Triple

T22050270
Position Surface form Disambiguated ID Type / Status
Subject Nüshu E544863 entity
Predicate ISO15924Code P208 FINISHED
Object Nshu
Nshu is the ISO 15924 code for Nüshu, a historic syllabic script from China traditionally used exclusively by women in parts of Hunan province.
E1515680 NE FINISHED

How this triple was built (4 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: Nshu | Statement: [Nüshu, ISO15924Code, Nshu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nshu
Context triple: [Nüshu, ISO15924Code, Nshu]
  • A. Tashelhiyt
    Tashelhiyt is a variety of the Shilha Berber language spoken primarily by Amazigh communities in southwestern Morocco.
  • B. Chazhashi
    Chazhashi is a historic village in Georgia’s Svaneti region, renowned for its medieval stone towers and traditional mountain architecture.
  • C. Chumashan
    Chumashan is a small family of extinct Native American languages once spoken along the southern and central coast of California.
  • D. Qrami
    Qrami is the Azerbaijani name for the Khrami River, a tributary of the Kura River flowing through Georgia and Azerbaijan.
  • E. Munduruku
    Munduruku is an indigenous people of the Brazilian Amazon known for their distinct language, rich cultural traditions, and historical prominence along the Tapajós River.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Nshu
Triple: [Nüshu, ISO15924Code, Nshu]
Generated description
Nshu is the ISO 15924 code for Nüshu, a historic syllabic script from China traditionally used exclusively by women in parts of Hunan province.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Nshu
Target entity description: Nshu is the ISO 15924 code for Nüshu, a historic syllabic script from China traditionally used exclusively by women in parts of Hunan province.
  • A. Tashelhiyt
    Tashelhiyt is a variety of the Shilha Berber language spoken primarily by Amazigh communities in southwestern Morocco.
  • B. Chazhashi
    Chazhashi is a historic village in Georgia’s Svaneti region, renowned for its medieval stone towers and traditional mountain architecture.
  • C. Chumashan
    Chumashan is a small family of extinct Native American languages once spoken along the southern and central coast of California.
  • D. Qrami
    Qrami is the Azerbaijani name for the Khrami River, a tributary of the Kura River flowing through Georgia and Azerbaijan.
  • E. Munduruku
    Munduruku is an indigenous people of the Brazilian Amazon known for their distinct language, rich cultural traditions, and historical prominence along the Tapajós River.
  • F. None of above. chosen

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_69e11e32445c8190ab97089b48a130bb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f128323fb08190b9592fd08a96cba0 completed April 28, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a7b79594c8190b66439167d7a3cbd completed May 18, 2026, 2:37 a.m.
NEDg Description generation batch_6a0a7cb2b0c88190930290efe1d8371a completed May 18, 2026, 2:42 a.m.
NED2 Entity disambiguation (via description) batch_6a0a7e131ae48190a564a86d3d691e1a completed May 18, 2026, 2:48 a.m.
Created at: April 16, 2026, 8:26 p.m.