Triple

T153442
Position Surface form Disambiguated ID Type / Status
Subject Right Livelihood Award E3479 entity
Predicate hasLaureate P1618 FINISHED
Object Sima Samar
Sima Samar is an Afghan physician and human rights advocate renowned for her work promoting women's rights, education, and social justice in Afghanistan.
 E21586 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: Sima Samar | Statement: [Right Livelihood Award, hasLaureate, Sima Samar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sima Samar
Context triple: [Right Livelihood Award, hasLaureate, Sima Samar]
  • A. Amara Namani
    Amara Namani is a young, resourceful Jaeger pilot and central protagonist in the science fiction film "Pacific Rim: Uprising."
  • B. Gondi
    Gondi is a Dravidian language spoken primarily by the Gondi people in central India, especially across parts of Madhya Pradesh, Maharashtra, Chhattisgarh, and neighboring regions.
  • C. Koba
    Koba was a revolutionary alias used by Joseph Stalin during his early political activities in the Bolshevik movement.
  • D. Aha Makhav
    Aha Makhav is the endonym used by the Mojave people to refer to themselves and their cultural identity.
  • E. Tamada
    Tamada is the traditional Georgian toastmaster who leads feasts and orchestrates toasts during the supra, Georgia’s ceremonial banquet.
  • 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: Sima Samar
Triple: [Right Livelihood Award, hasLaureate, Sima Samar]
Generated description
Sima Samar is an Afghan physician and human rights advocate renowned for her work promoting women's rights, education, and social justice in Afghanistan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sima Samar
Target entity description: Sima Samar is an Afghan physician and human rights advocate renowned for her work promoting women's rights, education, and social justice in Afghanistan.
  • A. Amara Namani
    Amara Namani is a young, resourceful Jaeger pilot and central protagonist in the science fiction film "Pacific Rim: Uprising."
  • B. Gondi
    Gondi is a Dravidian language spoken primarily by the Gondi people in central India, especially across parts of Madhya Pradesh, Maharashtra, Chhattisgarh, and neighboring regions.
  • C. Koba
    Koba was a revolutionary alias used by Joseph Stalin during his early political activities in the Bolshevik movement.
  • D. Aha Makhav
    Aha Makhav is the endonym used by the Mojave people to refer to themselves and their cultural identity.
  • E. Tamada
    Tamada is the traditional Georgian toastmaster who leads feasts and orchestrates toasts during the supra, Georgia’s ceremonial banquet.
  • 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_69a252868de4819080e21c9938bfe8b6 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a25810799c8190a515a39169126e46 completed Feb. 28, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2e7e0c7708190ac66e0a45f6782eb completed Feb. 28, 2026, 1:04 p.m.
NEDg Description generation batch_69a2e8f3e3c08190ae8c3f60eb530268 completed Feb. 28, 2026, 1:09 p.m.
NED2 Entity disambiguation (via description) batch_69a2e9deddf4819090917d418b8daecb completed Feb. 28, 2026, 1:13 p.m.
Created at: Feb. 28, 2026, 2:31 a.m.