Triple

T13297599
Position Surface form Disambiguated ID Type / Status
Subject Ty Law E316725 entity
Predicate givenName P17 FINISHED
Object Tajuan
Tajuan is the given first name of former NFL cornerback Ty Law.
 E1038047 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: Tajuan | Statement: [Ty Law, givenName, Tajuan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tajuan
Context triple: [Ty Law, givenName, Tajuan]
  • A. Taif
    Taif is a city in western Saudi Arabia known for its cool climate, rose cultivation, and historical significance as a summer resort and cultural center.
  • B. Tangtse
    Tangtse is a village in the Leh district of Ladakh, India, situated along key routes between the Indus Valley and the Pangong Tso region in the Himalayas.
  • C. Guting
    Guting is a key Taipei Metro station in central Taipei that serves as a transfer point between multiple subway lines.
  • D. Chamkani
    Chamkani is a Pashtun tribe traditionally associated with the Karlani tribal confederation in the Afghanistan–Pakistan border region.
  • E. Zaitian
    Zaitian was the personal name of the Guangxu Emperor, a late Qing dynasty ruler of China known for his attempted modernization reforms and his confinement under Empress Dowager Cixi.
  • 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: Tajuan
Triple: [Ty Law, givenName, Tajuan]
Generated description
Tajuan is the given first name of former NFL cornerback Ty Law.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tajuan
Target entity description: Tajuan is the given first name of former NFL cornerback Ty Law.
  • A. Taif
    Taif is a city in western Saudi Arabia known for its cool climate, rose cultivation, and historical significance as a summer resort and cultural center.
  • B. Tangtse
    Tangtse is a village in the Leh district of Ladakh, India, situated along key routes between the Indus Valley and the Pangong Tso region in the Himalayas.
  • C. Guting
    Guting is a key Taipei Metro station in central Taipei that serves as a transfer point between multiple subway lines.
  • D. Chamkani
    Chamkani is a Pashtun tribe traditionally associated with the Karlani tribal confederation in the Afghanistan–Pakistan border region.
  • E. Zaitian
    Zaitian was the personal name of the Guangxu Emperor, a late Qing dynasty ruler of China known for his attempted modernization reforms and his confinement under Empress Dowager Cixi.
  • 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_69d806b40ab4819094adf6c374f4811a completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d990a2f2708190a8f2aa7e7c0b92d2 completed April 11, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7305fd57881909c1d7f09f3c084cf completed May 3, 2026, 11:24 a.m.
NEDg Description generation batch_69f731b2794881909c55904842c6c9d6 completed May 3, 2026, 11:29 a.m.
NED2 Entity disambiguation (via description) batch_69f7320ee3108190bd8bc91d180c7cf4 completed May 3, 2026, 11:31 a.m.
Created at: April 9, 2026, 9:28 p.m.