Triple

T7894580
Position Surface form Disambiguated ID Type / Status
Subject PDP-7 E183314 entity
Predicate operatingSystem P1593 FINISHED
Object FOCAL
FOCAL is an early interactive programming language developed by Digital Equipment Corporation, commonly used on PDP-series minicomputers for scientific and educational purposes.
 E701311 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: FOCAL | Statement: [PDP-7, operatingSystem, FOCAL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FOCAL
Context triple: [PDP-7, operatingSystem, FOCAL]
  • A. FOCAS
    FOCAS is an optical camera and spectrograph instrument used on the Subaru Telescope for detailed imaging and spectroscopic observations of astronomical objects.
  • B. FOC
    FOC is the IATA airport code for Fuzhou Changle International Airport, the main airport serving Fuzhou in Fujian Province, China.
  • C. Fokus
    Fokus is the Norwegian Intelligence Service’s publicly released annual assessment report on global security and threat developments.
  • D. FOCs
    FOCs are rail freight operating companies in the United Kingdom that run cargo train services on the national rail network.
  • E. InFocus
    InFocus is an American company best known for designing and manufacturing digital projectors and other display technologies.
  • 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: FOCAL
Triple: [PDP-7, operatingSystem, FOCAL]
Generated description
FOCAL is an early interactive programming language developed by Digital Equipment Corporation, commonly used on PDP-series minicomputers for scientific and educational purposes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FOCAL
Target entity description: FOCAL is an early interactive programming language developed by Digital Equipment Corporation, commonly used on PDP-series minicomputers for scientific and educational purposes.
  • A. FOCAS
    FOCAS is an optical camera and spectrograph instrument used on the Subaru Telescope for detailed imaging and spectroscopic observations of astronomical objects.
  • B. FOC
    FOC is the IATA airport code for Fuzhou Changle International Airport, the main airport serving Fuzhou in Fujian Province, China.
  • C. Fokus
    Fokus is the Norwegian Intelligence Service’s publicly released annual assessment report on global security and threat developments.
  • D. FOCs
    FOCs are rail freight operating companies in the United Kingdom that run cargo train services on the national rail network.
  • E. InFocus
    InFocus is an American company best known for designing and manufacturing digital projectors and other display technologies.
  • 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_69ca828c474c8190a254d6499871eaff completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3a15a7e88190a05474844817e5d2 completed March 31, 2026, 3:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5bae4bdc8190be7db2ba3acb708a completed March 31, 2026, 5:29 a.m.
NEDg Description generation batch_69cb7631d10881908e3c7dacb98520cd completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbbf847f3c819092d690d8d65f6d60 completed March 31, 2026, 12:35 p.m.
Created at: March 30, 2026, 5:01 p.m.