Triple

T2597657
Position Surface form Disambiguated ID Type / Status
Subject Dhundhar E58269 entity
Predicate includesCity P3207 FINISHED
Object Amber
Amber is a historic town near Jaipur in Rajasthan, India, renowned for its hilltop Amber Fort and rich Rajput architectural heritage.
 E281917 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: Amber | Statement: [Dhundhar, includesCity, Amber]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Amber
Context triple: [Dhundhar, includesCity, Amber]
  • A. Amber
    Amber is a character from the film "Green Room," a tense horror-thriller about a punk band trapped in a remote venue controlled by violent neo-Nazis.
  • B. Opal
    Opal is a precious gemstone renowned for its vibrant play-of-color and is especially associated with major deposits in Australia.
  • C. Zipolite
    Zipolite is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its clothing-optional beach, bohemian vibe, and strong Pacific surf.
  • D. Beryl
    Beryl is a given name that can be used for people of any gender, historically more common as a female name in English-speaking countries.
  • E. Sapphire
    Sapphire is an American author best known for her novel "Push," which was adapted into the acclaimed film "Precious."
  • 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: Amber
Triple: [Dhundhar, includesCity, Amber]
Generated description
Amber is a historic town near Jaipur in Rajasthan, India, renowned for its hilltop Amber Fort and rich Rajput architectural heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Amber
Target entity description: Amber is a historic town near Jaipur in Rajasthan, India, renowned for its hilltop Amber Fort and rich Rajput architectural heritage.
  • A. Amber
    Amber is a character from the film "Green Room," a tense horror-thriller about a punk band trapped in a remote venue controlled by violent neo-Nazis.
  • B. Opal
    Opal is a precious gemstone renowned for its vibrant play-of-color and is especially associated with major deposits in Australia.
  • C. Zipolite
    Zipolite is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its clothing-optional beach, bohemian vibe, and strong Pacific surf.
  • D. Beryl
    Beryl is a given name that can be used for people of any gender, historically more common as a female name in English-speaking countries.
  • E. Sapphire
    Sapphire is an American author best known for her novel "Push," which was adapted into the acclaimed film "Precious."
  • 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_69ab4ac14040819098b13f4a27d5c8ff completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd4548b1081909d28f88ea5e14202 completed March 7, 2026, 7:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69af83cc45f8819099d581725a53e527 completed March 10, 2026, 2:37 a.m.
NEDg Description generation batch_69af84a08ffc8190b113d07f3d322d0c completed March 10, 2026, 2:40 a.m.
NED2 Entity disambiguation (via description) batch_69af85a052448190898ee1a2f8368122 completed March 10, 2026, 2:44 a.m.
Created at: March 6, 2026, 9:49 p.m.