Triple

T3004982
Position Surface form Disambiguated ID Type / Status
Subject Shina E81878 entity
Predicate hasDialect P4251 FINISHED
Object Haramosh Shina
Haramosh Shina is a regional dialect of the Shina language spoken in the Haramosh area of northern Pakistan.
E322158 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: Haramosh Shina | Statement: [Shina, hasDialect, Haramosh Shina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haramosh Shina
Context triple: [Shina, hasDialect, Haramosh Shina]
  • A. Takanami
    Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
  • B. Shōhō
    Shōhō was a Japanese light aircraft carrier of the Imperial Japanese Navy during World War II, notable for being the first Japanese carrier sunk in the war during the Battle of the Coral Sea.
  • C. Shinpei
    Shinpei is a Japanese given name commonly used for males and borne by various notable figures in politics, arts, and entertainment.
  • D. Kintomo Mushakoji
    Kintomo Mushakoji was a Japanese diplomat who served as a key representative of Japan’s government in the 1930s, notably involved in its alignment with Axis powers.
  • E. Yasu
    Yasu is a Japanese city located in Shiga Prefecture, known for its blend of residential areas, local industry, and proximity to Lake Biwa.
  • 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: Haramosh Shina
Triple: [Shina, hasDialect, Haramosh Shina]
Generated description
Haramosh Shina is a regional dialect of the Shina language spoken in the Haramosh area of northern Pakistan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Haramosh Shina
Target entity description: Haramosh Shina is a regional dialect of the Shina language spoken in the Haramosh area of northern Pakistan.
  • A. Takanami
    Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
  • B. Shōhō
    Shōhō was a Japanese light aircraft carrier of the Imperial Japanese Navy during World War II, notable for being the first Japanese carrier sunk in the war during the Battle of the Coral Sea.
  • C. Shinpei
    Shinpei is a Japanese given name commonly used for males and borne by various notable figures in politics, arts, and entertainment.
  • D. Kintomo Mushakoji
    Kintomo Mushakoji was a Japanese diplomat who served as a key representative of Japan’s government in the 1930s, notably involved in its alignment with Axis powers.
  • E. Yasu
    Yasu is a Japanese city located in Shiga Prefecture, known for its blend of residential areas, local industry, and proximity to Lake Biwa.
  • 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_69ad8b1c4de88190a83b7cefaa1f2842 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9a15ad9c81908255003bdb38d603 completed March 8, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1eee3e7dc8190941407d73fc56e0f completed March 11, 2026, 10:38 p.m.
NEDg Description generation batch_69b1ef69d49c81908caa41a80718896b completed March 11, 2026, 10:40 p.m.
NED2 Entity disambiguation (via description) batch_69b1f05e44e08190be8b194938b6c1c7 completed March 11, 2026, 10:44 p.m.
Created at: March 8, 2026, 2:59 p.m.