Triple

T11040176
Position Surface form Disambiguated ID Type / Status
Subject Kazakh Khanate E260992 entity
Predicate capital P234 FINISHED
Object Sygnaq
Sygnaq was a historic Central Asian city that served as an important political and trade center, notably functioning as the capital of the Kazakh Khanate.
 E900842 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: Sygnaq | Statement: [Kazakh Khanate, capital, Sygnaq]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sygnaq
Context triple: [Kazakh Khanate, capital, Sygnaq]
  • A. Innogen
    Innogen is the original name of Imogen, the virtuous and wronged heroine of William Shakespeare’s play "Cymbeline."
  • B. Sygma
    Sygma is a musical artist associated with the track "Love Lies."
  • C. Kymab
    Kymab is a biotechnology company based in Cambridge, UK, focused on developing antibody-based therapeutics using its proprietary transgenic mouse platforms.
  • D. Morgex
    Morgex is a small Alpine town and comune in Italy’s Aosta Valley, known for its mountain scenery and production of high-altitude white wines.
  • E. Signum
    Signum is a Swiss train protection and signaling system used to enhance the safety and control of railway operations.
  • 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: Sygnaq
Triple: [Kazakh Khanate, capital, Sygnaq]
Generated description
Sygnaq was a historic Central Asian city that served as an important political and trade center, notably functioning as the capital of the Kazakh Khanate.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sygnaq
Target entity description: Sygnaq was a historic Central Asian city that served as an important political and trade center, notably functioning as the capital of the Kazakh Khanate.
  • A. Innogen
    Innogen is the original name of Imogen, the virtuous and wronged heroine of William Shakespeare’s play "Cymbeline."
  • B. Sygma
    Sygma is a musical artist associated with the track "Love Lies."
  • C. Kymab
    Kymab is a biotechnology company based in Cambridge, UK, focused on developing antibody-based therapeutics using its proprietary transgenic mouse platforms.
  • D. Morgex
    Morgex is a small Alpine town and comune in Italy’s Aosta Valley, known for its mountain scenery and production of high-altitude white wines.
  • E. Signum
    Signum is a Swiss train protection and signaling system used to enhance the safety and control of railway operations.
  • 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_69d6aa979bdc8190bf0e79104cc098c1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797ff519481909ebc2515b3d241de completed April 9, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3a9d61b548190949f0dfbcb782064 completed April 18, 2026, 3:57 p.m.
NEDg Description generation batch_69e3ad024ee88190948d5d1c327fd063 completed April 18, 2026, 4:10 p.m.
NED2 Entity disambiguation (via description) batch_69e3b1fff754819092d634f46fb42387 completed April 18, 2026, 4:32 p.m.
Created at: April 8, 2026, 9:26 p.m.