Triple

T21727036
Position Surface form Disambiguated ID Type / Status
Subject PDP-10 E536299 entity
Predicate notableModel P1503 FINISHED
Object KI10
The KI10 is a model of Digital Equipment Corporation's PDP-10 mainframe computer series, known for its enhanced performance and use in time-sharing systems during the 1970s.
 E1498025 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: KI10 | Statement: [PDP-10, notableModel, KI10]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KI10
Context triple: [PDP-10, notableModel, KI10]
  • A. KI
    KI is the ISO 3166-1 alpha-2 country code for Kiribati, a Pacific island nation.
  • B. KI
    KI is the regional vehicle registration code used on license plates to identify vehicles registered in Kyiv Oblast, Ukraine.
  • C. KI
    KI is the vehicle registration code used on license plates for the German city of Kiel.
  • D. KI
    KI is the abbreviation for the Karolinska Institute, a renowned Swedish medical university known for its leading research and role in selecting Nobel laureates in Physiology or Medicine.
  • E. K1
    K1 is the first highly accurate marine chronometer built by Larcum Kendall in the 18th century, famous for its role in improving longitude determination at sea.
  • 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: KI10
Triple: [PDP-10, notableModel, KI10]
Generated description
The KI10 is a model of Digital Equipment Corporation's PDP-10 mainframe computer series, known for its enhanced performance and use in time-sharing systems during the 1970s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KI10
Target entity description: The KI10 is a model of Digital Equipment Corporation's PDP-10 mainframe computer series, known for its enhanced performance and use in time-sharing systems during the 1970s.
  • A. KI
    KI is the ISO 3166-1 alpha-2 country code for Kiribati, a Pacific island nation.
  • B. KI
    KI is the abbreviation for the Karolinska Institute, a renowned Swedish medical university known for its leading research and role in selecting Nobel laureates in Physiology or Medicine.
  • C. KI
    KI is the regional vehicle registration code used on license plates to identify vehicles registered in Kyiv Oblast, Ukraine.
  • D. KI
    KI is the vehicle registration code used on license plates for the German city of Kiel.
  • E. K1
    K1 is the first highly accurate marine chronometer built by Larcum Kendall in the 18th century, famous for its role in improving longitude determination at sea.
  • 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_69e0c46d3284819099a4f9d5a704eb95 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69efd973ac648190bb09e20ac1be2d9b completed April 27, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a24bd96a881908fdef9990e8dc2a9 completed May 17, 2026, 8:27 p.m.
NEDg Description generation batch_6a0a2752f29c81909dca3f8a54510b2c completed May 17, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_6a0a27cb1e0c8190a0eafacb680552d9 completed May 17, 2026, 8:40 p.m.
Created at: April 16, 2026, 6:48 p.m.