Triple

T2381110
Position Surface form Disambiguated ID Type / Status
Subject Wanetsi E46312 entity
Predicate hasAlternativeName P39 FINISHED
Object Tareeno
Tareeno is an alternative name for Wanetsi, an Eastern Iranian language closely related to Pashto and spoken primarily in parts of Pakistan and Afghanistan.
 E261641 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: Tareeno | Statement: [Wanetsi, hasAlternativeName, Tareeno]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tareeno
Context triple: [Wanetsi, hasAlternativeName, Tareeno]
  • A. Humera
    Humera is a town in northwestern Ethiopia near the borders with Eritrea and Sudan, known for its strategic location and sesame production.
  • B. Zabana
    Zabana is an Oceanic language spoken in the Solomon Islands, primarily on Santa Isabel Island.
  • C. Rawdah
    Rawdah is the revered area between the Prophet Muhammad’s tomb and his pulpit in Al-Masjid an-Nabawi in Medina, considered one of the holiest sites in Islam.
  • D. Mihna
    The Mihna was an Islamic inquisition instituted in the 9th century that tested and persecuted scholars over their adherence to the doctrine of the createdness of the Qur’an.
  • E. Shabana
    Shabana is a prominent Bangladeshi film actress renowned for her extensive and influential career in Bengali cinema.
  • 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: Tareeno
Triple: [Wanetsi, hasAlternativeName, Tareeno]
Generated description
Tareeno is an alternative name for Wanetsi, an Eastern Iranian language closely related to Pashto and spoken primarily in parts of Pakistan and Afghanistan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tareeno
Target entity description: Tareeno is an alternative name for Wanetsi, an Eastern Iranian language closely related to Pashto and spoken primarily in parts of Pakistan and Afghanistan.
  • A. Humera
    Humera is a town in northwestern Ethiopia near the borders with Eritrea and Sudan, known for its strategic location and sesame production.
  • B. Zabana
    Zabana is an Oceanic language spoken in the Solomon Islands, primarily on Santa Isabel Island.
  • C. Rawdah
    Rawdah is the revered area between the Prophet Muhammad’s tomb and his pulpit in Al-Masjid an-Nabawi in Medina, considered one of the holiest sites in Islam.
  • D. Mihna
    The Mihna was an Islamic inquisition instituted in the 9th century that tested and persecuted scholars over their adherence to the doctrine of the createdness of the Qur’an.
  • E. Shabana
    Shabana is a prominent Bangladeshi film actress renowned for her extensive and influential career in Bengali cinema.
  • 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_69a88a1554a48190a0180682bcf099be completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc7b7c9188190a824e4b469bc1548 completed March 7, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea8b4f85c81909e5a4eda271b73ca completed March 9, 2026, 11:02 a.m.
NEDg Description generation batch_69aeac747e488190aea9b1831ea748a8 completed March 9, 2026, 11:18 a.m.
NED2 Entity disambiguation (via description) batch_69aeadb6008481909bf1e5d1210a58b8 completed March 9, 2026, 11:23 a.m.
Created at: March 4, 2026, 7:57 p.m.