Triple

T6957135
Position Surface form Disambiguated ID Type / Status
Subject Pordenone E161271 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object PN
PN is the vehicle registration code used for the Italian city and province of Pordenone in the Friuli Venezia Giulia region.
 E631202 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: PN | Statement: [Pordenone, vehicleRegistrationCode, PN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PN
Context triple: [Pordenone, vehicleRegistrationCode, PN]
  • A. PN
    PN is the official abbreviation for the Philippine Navy, the naval warfare branch of the Armed Forces of the Philippines.
  • B. PNP
    The PNP is the national law enforcement agency of Peru responsible for maintaining public order, preventing and investigating crime, and ensuring internal security across the country.
  • C. P
    P is the vehicle registration code used on license plates for the Czech city of Plzeň.
  • D. P
    P is the vehicle registration code used on license plates for the Lithuanian city of Panevėžys.
  • E. PJ
    PJ is the common abbreviation for the Argentine Justicialist Party, a major Peronist political party in Argentina.
  • 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: PN
Triple: [Pordenone, vehicleRegistrationCode, PN]
Generated description
PN is the vehicle registration code used for the Italian city and province of Pordenone in the Friuli Venezia Giulia region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PN
Target entity description: PN is the vehicle registration code used for the Italian city and province of Pordenone in the Friuli Venezia Giulia region.
  • A. PN
    PN is the official abbreviation for the Philippine Navy, the naval warfare branch of the Armed Forces of the Philippines.
  • B. PNP
    The PNP is the national law enforcement agency of Peru responsible for maintaining public order, preventing and investigating crime, and ensuring internal security across the country.
  • C. P
    P is the vehicle registration code used on license plates for the Lithuanian city of Panevėžys.
  • D. P
    P is the vehicle registration code used on license plates for the Czech city of Plzeň.
  • E. PJ
    PJ is a musical artist known for contributing guest performances to other musicians’ tracks.
  • 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_69c68852a9a0819097797e31d492e273 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dad0e52081908b524dc6a66bab01 completed March 27, 2026, 7:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7588e2c5c8190a66a0205f3c2bf99 completed March 28, 2026, 4:26 a.m.
NEDg Description generation batch_69c7598d785881909a79ec6be6546a1c completed March 28, 2026, 4:31 a.m.
NED2 Entity disambiguation (via description) batch_69c75a0b33788190a120c42f901ac56e completed March 28, 2026, 4:33 a.m.
Created at: March 27, 2026, 2:29 p.m.