Triple

T8492980
Position Surface form Disambiguated ID Type / Status
Subject Reason E201017 entity
Predicate hasRobotCharacter P12208 FINISHED
Object QT-1
QT-1 is a robot character from Isaac Asimov’s science fiction story “Reason,” notable for its philosophical questioning of reality and its creators.
 E737159 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: QT-1 | Statement: [Reason, hasRobotCharacter, QT-1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: QT-1
Context triple: [Reason, hasRobotCharacter, QT-1]
  • A. QTA
    QTA is the official railway station code for Quetta Railway Station in Pakistan’s railway network.
  • B. RQTC
    RQTC is the commonly used abbreviation for the Rose Quarter Transit Center, a major public transportation hub in Portland, Oregon.
  • C. KT-1B Wongbee
    The KT-1B Wongbee is a South Korean–designed turboprop trainer aircraft variant used primarily for basic flight training and aerobatics.
  • D. YQT
    YQT is the IATA airport code for Thunder Bay International Airport, a regional air transport hub in Thunder Bay, Ontario, Canada.
  • E. SQT
    SQT is the intensive advanced training course that prepares U.S. Navy SEAL candidates for operational deployment after completing Basic Underwater Demolition/SEAL (BUD/S) training.
  • 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: QT-1
Triple: [Reason, hasRobotCharacter, QT-1]
Generated description
QT-1 is a robot character from Isaac Asimov’s science fiction story “Reason,” notable for its philosophical questioning of reality and its creators.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: QT-1
Target entity description: QT-1 is a robot character from Isaac Asimov’s science fiction story “Reason,” notable for its philosophical questioning of reality and its creators.
  • A. QTA
    QTA is the official railway station code for Quetta Railway Station in Pakistan’s railway network.
  • B. RQTC
    RQTC is the commonly used abbreviation for the Rose Quarter Transit Center, a major public transportation hub in Portland, Oregon.
  • C. KT-1B Wongbee
    The KT-1B Wongbee is a South Korean–designed turboprop trainer aircraft variant used primarily for basic flight training and aerobatics.
  • D. YQT
    YQT is the IATA airport code for Thunder Bay International Airport, a regional air transport hub in Thunder Bay, Ontario, Canada.
  • E. SQT
    SQT is the intensive advanced training course that prepares U.S. Navy SEAL candidates for operational deployment after completing Basic Underwater Demolition/SEAL (BUD/S) training.
  • 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_69ca831ee390819095fae73400bbfafc completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe579b7088190b297b04527e36a2b completed March 31, 2026, 3:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce3a5c260c8190bc7012a04363d260 completed April 2, 2026, 9:43 a.m.
NEDg Description generation batch_69ce3ca3be5c8190844e54805e9acaeb completed April 2, 2026, 9:53 a.m.
NED2 Entity disambiguation (via description) batch_69ce3d4e92e88190a90ba1567c569b00 completed April 2, 2026, 9:56 a.m.
Created at: March 30, 2026, 6:13 p.m.