Triple

T5480301
Position Surface form Disambiguated ID Type / Status
Subject RTP E123450 entity
Predicate hasProfile P9523 FINISHED
Object AVPF
AVPF (Audio-Visual Profile with Feedback) is an RTP profile that extends the basic audio-visual profile with enhanced feedback and control mechanisms to improve real-time media transmission quality.
 E523059 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: AVPF | Statement: [RTP, hasProfile, AVPF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AVPF
Context triple: [RTP, hasProfile, AVPF]
  • A. AVV
    AVV is the IATA airport code for Avalon Airport, a regional airport serving the Geelong and Melbourne areas in Victoria, Australia.
  • B. AVP
    AVP is the three-letter IATA airport code for Wilkes-Barre/Scranton International Airport in Pennsylvania, USA.
  • C. AFT
    AFT is a major American labor union representing teachers and other education professionals across the United States.
  • D. PFA
    PFA is a Danish pension fund that invests in large infrastructure projects such as offshore wind farms.
  • E. PFA
    PFA is the Professional Footballers' Association, the trade union and representative body for professional footballers in England and Wales.
  • 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: AVPF
Triple: [RTP, hasProfile, AVPF]
Generated description
AVPF (Audio-Visual Profile with Feedback) is an RTP profile that extends the basic audio-visual profile with enhanced feedback and control mechanisms to improve real-time media transmission quality.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: AVPF
Target entity description: AVPF (Audio-Visual Profile with Feedback) is an RTP profile that extends the basic audio-visual profile with enhanced feedback and control mechanisms to improve real-time media transmission quality.
  • A. AVV
    AVV is the IATA airport code for Avalon Airport, a regional airport serving the Geelong and Melbourne areas in Victoria, Australia.
  • B. AVP
    AVP is the three-letter IATA airport code for Wilkes-Barre/Scranton International Airport in Pennsylvania, USA.
  • C. AFT
    AFT is a major American labor union representing teachers and other education professionals across the United States.
  • D. PFA
    PFA is a Danish pension fund that invests in large infrastructure projects such as offshore wind farms.
  • E. PFA
    PFA is the Professional Footballers' Association, the trade union and representative body for professional footballers in England and Wales.
  • 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_69bd4648883481909e9775d43300c5fa completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd9248ca348190aa116cace0f9b07a completed March 20, 2026, 6:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf48a2880c8190ad76cf8c3862aede completed March 22, 2026, 1:40 a.m.
NEDg Description generation batch_69bf4a95375881909ba730ad108eee8b completed March 22, 2026, 1:49 a.m.
NED2 Entity disambiguation (via description) batch_69bf4afb47a88190a66de6b6c7d5c241 completed March 22, 2026, 1:50 a.m.
Created at: March 20, 2026, 2:09 p.m.