Triple

T22658169
Position Surface form Disambiguated ID Type / Status
Subject رشدي أباظة E559286 entity
Predicate notableWork P4 FINISHED
Object تمر حنة
تمر حنة هو فيلم مصري شهير من أفلام الخمسينيات يُعد من أبرز الأعمال الاستعراضية والرومانسية في تاريخ السينما المصرية.
 E1548016 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: تمر حنة | Statement: [رشدي أباظة, notableWork, تمر حنة]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: تمر حنة
Context triple: [رشدي أباظة, notableWork, تمر حنة]
  • A. Dar Chaabane
    Dar Chaabane is a coastal town in northeastern Tunisia known for its traditional architecture and proximity to the Mediterranean Sea.
  • B. 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.
  • C. Umamah
    Umamah is a female given name of Arabic origin, historically borne by a granddaughter of the Prophet Muhammad.
  • D. Nahan
    Nahan is a small hill town and municipal council in Himachal Pradesh, India, known for its scenic surroundings, pleasant climate, and role as a regional administrative and commercial center.
  • E. Al-Mumtahanah
    Al-Mumtahanah is the 60th chapter of the Qur’an, known for its guidance on relations with non-Muslims and the testing of faith among believers.
  • 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: تمر حنة
Triple: [رشدي أباظة, notableWork, تمر حنة]
Generated description
تمر حنة هو فيلم مصري شهير من أفلام الخمسينيات يُعد من أبرز الأعمال الاستعراضية والرومانسية في تاريخ السينما المصرية.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: تمر حنة
Target entity description: تمر حنة هو فيلم مصري شهير من أفلام الخمسينيات يُعد من أبرز الأعمال الاستعراضية والرومانسية في تاريخ السينما المصرية.
  • A. Dar Chaabane
    Dar Chaabane is a coastal town in northeastern Tunisia known for its traditional architecture and proximity to the Mediterranean Sea.
  • B. 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.
  • C. Umamah
    Umamah is a female given name of Arabic origin, historically borne by a granddaughter of the Prophet Muhammad.
  • D. Nahan
    Nahan is a small hill town and municipal council in Himachal Pradesh, India, known for its scenic surroundings, pleasant climate, and role as a regional administrative and commercial center.
  • E. Al-Mumtahanah
    Al-Mumtahanah is the 60th chapter of the Qur’an, known for its guidance on relations with non-Muslims and the testing of faith among believers.
  • 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_69e245489dd88190b1f674acf61c8769 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f1765d10588190b4574f3e64617cd4 completed April 29, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b586cd2b8819095ff984b9c14c1ec completed May 18, 2026, 6:20 p.m.
NEDg Description generation batch_6a0b6f5f8bd08190b325b9dd91e199e1 completed May 18, 2026, 7:58 p.m.
NED2 Entity disambiguation (via description) batch_6a0b7044c94c81909a43b04f40af2a15 completed May 18, 2026, 8:02 p.m.
Created at: April 17, 2026, 3:07 p.m.