Triple

T14430188
Position Surface form Disambiguated ID Type / Status
Subject Арбатско-Покровская линия E357804 entity
Predicate имеетСтанцию P726 FINISHED
Object Волоколамская
«Волоколамская» — станция Московского метрополитена, расположенная на северо-западе города и обслуживающая жилые районы вблизи Волоколамского шоссе.
 E1100825 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: Волоколамская | Statement: [Арбатско-Покровская линия, имеетСтанцию, Волоколамская]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Волоколамская
Context triple: [Арбатско-Покровская линия, имеетСтанцию, Волоколамская]
  • A. Serpukhov
    Serpukhov is a historic Russian town south of Moscow known for its medieval monasteries, industrial heritage, and location on the Nara River.
  • B. Lyubertsy
    Lyubertsy is a city in Russia that serves as a major suburban and industrial center just southeast of Moscow.
  • C. Митино
    Митино — это станция Московского метрополитена, расположенная в одноимённом районе на северо-западе города.
  • D. Noginsk
    Noginsk is a town in western Russia that serves as an industrial and transport center east of Moscow.
  • E. Serpukhovskaya
    Serpukhovskaya is a Moscow Metro station serving the Serpukhovsko–Timiryazevskaya Line in central Moscow.
  • 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: Волоколамская
Triple: [Арбатско-Покровская линия, имеетСтанцию, Волоколамская]
Generated description
«Волоколамская» — станция Московского метрополитена, расположенная на северо-западе города и обслуживающая жилые районы вблизи Волоколамского шоссе.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Волоколамская
Target entity description: «Волоколамская» — станция Московского метрополитена, расположенная на северо-западе города и обслуживающая жилые районы вблизи Волоколамского шоссе.
  • A. Serpukhov
    Serpukhov is a historic Russian town south of Moscow known for its medieval monasteries, industrial heritage, and location on the Nara River.
  • B. Lyubertsy
    Lyubertsy is a city in Russia that serves as a major suburban and industrial center just southeast of Moscow.
  • C. Митино
    Митино — это станция Московского метрополитена, расположенная в одноимённом районе на северо-западе города.
  • D. Noginsk
    Noginsk is a town in western Russia that serves as an industrial and transport center east of Moscow.
  • E. Serpukhovskaya
    Serpukhovskaya is a Moscow Metro station serving the Serpukhovsko–Timiryazevskaya Line in central Moscow.
  • 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_69d8279402a88190821ffa39ae15bccf completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de914570f08190b1c7c1c57a0cb476 completed April 14, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd64898c088190ab4eef32ca4f5ed6 completed May 8, 2026, 4:20 a.m.
NEDg Description generation batch_69fd666a21d48190932a0a91f81490b4 completed May 8, 2026, 4:28 a.m.
NED2 Entity disambiguation (via description) batch_69fd66d75974819084aa4eb48f7079a3 completed May 8, 2026, 4:30 a.m.
Created at: April 10, 2026, 1:18 a.m.