Triple

T20317024
Position Surface form Disambiguated ID Type / Status
Subject State Second Pension E510405 entity
Predicate alsoKnownAs P39 FINISHED
Object S2P
S2P is the UK’s former State Second Pension, an additional earnings-related state benefit that supplemented the basic State Pension before being replaced by the new State Pension system.
E1423605 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: S2P | Statement: [State Second Pension, alsoKnownAs, S2P]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: S2P
Context triple: [State Second Pension, alsoKnownAs, S2P]
  • A. S2
    S2 is a line of the Munich S-Bahn rapid transit network that runs through the central trunk route and serves suburban areas around Munich.
  • B. S2
    S2 is one of the commuter rail lines of the Nuremberg S-Bahn network in Germany, serving suburban and regional routes around the city.
  • C. S2
    S2 is a line of Berlin's S-Bahn rapid transit network that connects northern and southern suburbs through the city center.
  • D. S2
    S2 is a commuter rail line of the Stuttgart S-Bahn network serving the Stuttgart metropolitan area in Germany.
  • E. S2
    S2 is a commuter rail line within Germany’s Rhine-Ruhr S-Bahn network, connecting various cities and suburbs in the densely populated Rhine-Ruhr metropolitan region.
  • 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: S2P
Triple: [State Second Pension, alsoKnownAs, S2P]
Generated description
S2P is the UK’s former State Second Pension, an additional earnings-related state benefit that supplemented the basic State Pension before being replaced by the new State Pension system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: S2P
Target entity description: S2P is the UK’s former State Second Pension, an additional earnings-related state benefit that supplemented the basic State Pension before being replaced by the new State Pension system.
  • A. S2
    S2 is a commuter rail line within Germany’s Rhine-Ruhr S-Bahn network, connecting various cities and suburbs in the densely populated Rhine-Ruhr metropolitan region.
  • B. S2
    S2 is the station code for Sala Daeng Station on Bangkok's BTS Skytrain system.
  • C. S2
    S2 is a line of Berlin's S-Bahn rapid transit network that connects northern and southern suburbs through the city center.
  • D. S2
    S2 is one of the commuter rail lines of the Nuremberg S-Bahn network in Germany, serving suburban and regional routes around the city.
  • E. S2
    S2 is a line of the Munich S-Bahn rapid transit network that runs through the central trunk route and serves suburban areas around Munich.
  • 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_69e0b4c7491c8190961113c4283b10b0 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e67788ca3c8190a3496fd54a5870d6 completed April 20, 2026, 6:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0861246f188190a0ffb84a61a7d8c1 completed May 16, 2026, 12:20 p.m.
NEDg Description generation batch_6a08627316d48190ab6f2f2cedd9b1db completed May 16, 2026, 12:26 p.m.
NED2 Entity disambiguation (via description) batch_6a086317d0888190809268c3b8b95f6f completed May 16, 2026, 12:29 p.m.
Created at: April 16, 2026, 11:19 a.m.