Triple

T7429015
Position Surface form Disambiguated ID Type / Status
Subject Lion Bridge E171438 entity
Predicate alsoKnownAs P39 FINISHED
Object Lviny Most
Lviny Most is a historic pedestrian bridge in Saint Petersburg, Russia, notable for its distinctive cast-iron lion sculptures.
 E663697 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: Lviny Most | Statement: [Lion Bridge, alsoKnownAs, Lviny Most]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lviny Most
Context triple: [Lion Bridge, alsoKnownAs, Lviny Most]
  • A. Orlík nad Vltavou
    Orlík nad Vltavou is a small Czech municipality on the Vltava River, known for its historic Orlík Castle and its association with the noble Schwarzenberg family.
  • B. Vávrová
    Vávrová is a Czech surname most notably borne by Dana Vávrová, a well-known Czech-German actress and film director.
  • C. Svitava
    Svitava is a river in the Czech Republic that flows through the city of Brno and is one of its main waterways.
  • D. Malé Poříčí
    Malé Poříčí is a small village and administrative part of the town of Náchod in the Hradec Králové Region of the Czech Republic.
  • E. Nýřany
    Nýřany is a town in the western Czech Republic, located near Plzeň and known historically for its coal mining and industrial development.
  • 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: Lviny Most
Triple: [Lion Bridge, alsoKnownAs, Lviny Most]
Generated description
Lviny Most is a historic pedestrian bridge in Saint Petersburg, Russia, notable for its distinctive cast-iron lion sculptures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lviny Most
Target entity description: Lviny Most is a historic pedestrian bridge in Saint Petersburg, Russia, notable for its distinctive cast-iron lion sculptures.
  • A. Orlík nad Vltavou
    Orlík nad Vltavou is a small Czech municipality on the Vltava River, known for its historic Orlík Castle and its association with the noble Schwarzenberg family.
  • B. Vávrová
    Vávrová is a Czech surname most notably borne by Dana Vávrová, a well-known Czech-German actress and film director.
  • C. Svitava
    Svitava is a river in the Czech Republic that flows through the city of Brno and is one of its main waterways.
  • D. Malé Poříčí
    Malé Poříčí is a small village and administrative part of the town of Náchod in the Hradec Králové Region of the Czech Republic.
  • E. Nýřany
    Nýřany is a town in the western Czech Republic, located near Plzeň and known historically for its coal mining and industrial development.
  • 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_69c68a63491881909281f73d4d5643bf completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f3082f188190af5673d18ac7e87e completed March 27, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69c81f0e28e88190805108dff740dda3 completed March 28, 2026, 6:33 p.m.
NEDg Description generation batch_69c8200c52c8819083b14e8d768fc9be completed March 28, 2026, 6:38 p.m.
NED2 Entity disambiguation (via description) batch_69c82084879c8190ae60b99f702dc058 completed March 28, 2026, 6:40 p.m.
Created at: March 27, 2026, 3:12 p.m.