Triple

T14906078
Position Surface form Disambiguated ID Type / Status
Subject Nokia N93 E360133 entity
Predicate modelNumber P8607 FINISHED
Object N93
The N93 is a Nokia smartphone from the mid-2000s known for its swivel design and advanced video recording capabilities for its time.
E1127605 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: N93 | Statement: [Nokia N93, modelNumber, N93]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: N93
Context triple: [Nokia N93, modelNumber, N93]
  • A. N92
    N92 is a Nokia smartphone model known for its early support of mobile TV and multimedia features.
  • B. N98
    N98 is the ICAO airport code assigned to Boyne City Municipal Airport, a public general aviation facility serving Boyne City, Michigan.
  • C. N98
    N98 is the Federal Aviation Administration (FAA) location identifier assigned to Boyne City Municipal Airport in Michigan, United States.
  • D. N9
    N9 is the abbreviated designation for N9 Warfare Systems, a defense-related organization focused on the development and integration of advanced warfare technologies and capabilities.
  • E. N9
    N9 is a national route in South Africa that connects several inland towns and links with major highways such as the N2.
  • 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: N93
Triple: [Nokia N93, modelNumber, N93]
Generated description
The N93 is a Nokia smartphone from the mid-2000s known for its swivel design and advanced video recording capabilities for its time.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: N93
Target entity description: The N93 is a Nokia smartphone from the mid-2000s known for its swivel design and advanced video recording capabilities for its time.
  • A. N92
    N92 is a Nokia smartphone model known for its early support of mobile TV and multimedia features.
  • B. N98
    N98 is the ICAO airport code assigned to Boyne City Municipal Airport, a public general aviation facility serving Boyne City, Michigan.
  • C. N98
    N98 is the Federal Aviation Administration (FAA) location identifier assigned to Boyne City Municipal Airport in Michigan, United States.
  • D. N9
    N9 is a national route in South Africa that connects several inland towns and links with major highways such as the N2.
  • E. N9
    N9 is the abbreviated designation for N9 Warfare Systems, a defense-related organization focused on the development and integration of advanced warfare technologies and capabilities.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded60cd5588190b1efecc2b220da69 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe72b7640c8190b463f69fb67fd30a completed May 8, 2026, 11:33 p.m.
NEDg Description generation batch_69fe771e361481908b58eb38f804f650 completed May 8, 2026, 11:51 p.m.
NED2 Entity disambiguation (via description) batch_69fe77fef3d88190afbd7839c4625226 completed May 8, 2026, 11:55 p.m.
Created at: April 10, 2026, 2:12 a.m.