Triple

T625624
Position Surface form Disambiguated ID Type / Status
Subject Maui E15811 entity
Predicate hasSettlement P1068 FINISHED
Object Hana
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
 E100137 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: Hana | Statement: [Maui, hasSettlement, Hana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hana
Context triple: [Maui, hasSettlement, Hana]
  • A. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • B. Haya
    Haya is a feminine given name of Arabic origin, commonly used in the Middle East and among Arabic-speaking communities.
  • C. Minna
    Minna is a major city and administrative center in north-central Nigeria, known as the capital of Niger State and a regional hub for trade and transportation.
  • D. Michiko
    Michiko is the former Empress of Japan and the wife of Emperor Emeritus Akihito, known for being the first commoner to marry into the Japanese imperial family.
  • E. Ahirani
    Ahirani is an Indo-Aryan dialect spoken primarily in the Khandesh region of Maharashtra, India, closely related to Marathi but with distinct phonological and lexical features.
  • 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: Hana
Triple: [Maui, hasSettlement, Hana]
Generated description
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hana
Target entity description: Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
  • A. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • B. Haya
    Haya is a feminine given name of Arabic origin, commonly used in the Middle East and among Arabic-speaking communities.
  • C. Minna
    Minna is a major city and administrative center in north-central Nigeria, known as the capital of Niger State and a regional hub for trade and transportation.
  • D. Michiko
    Michiko is the former Empress of Japan and the wife of Emperor Emeritus Akihito, known for being the first commoner to marry into the Japanese imperial family.
  • E. Ahirani
    Ahirani is an Indo-Aryan dialect spoken primarily in the Khandesh region of Maharashtra, India, closely related to Marathi but with distinct phonological and lexical features.
  • 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_69a4935c131c8190a5378c6bf101e8cc completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49e574444819087999404f3e3ffd9 completed March 1, 2026, 8:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7927a25b081909b553fe3a486e84a completed March 4, 2026, 2:01 a.m.
NEDg Description generation batch_69a796fc8de08190a8bc1fff36d3ea9d completed March 4, 2026, 2:20 a.m.
NED2 Entity disambiguation (via description) batch_69a79773ce988190bd019e1bd03a3464 completed March 4, 2026, 2:22 a.m.
Created at: March 1, 2026, 7:35 p.m.