Triple

T15429093
Position Surface form Disambiguated ID Type / Status
Subject Milan Metro Line 3 E369587 entity
Predicate station P726 FINISHED
Object Zara
Zara is a station on Milan’s Metro Line 3, serving as a public transportation stop within the city’s underground network.
 E1155604 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: Zara | Statement: [Milan Metro Line 3, station, Zara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zara
Context triple: [Milan Metro Line 3, station, Zara]
  • A. Zara
    Zara is the historical Italian name for the coastal Croatian city of Zadar on the Adriatic Sea.
  • B. Zara
    Zara is a character in the 1953 film noir "Pickup on South Street," involved in the story’s underworld of espionage and crime.
  • C. Zara
    Zara is a global fast-fashion retail brand known for rapidly translating runway trends into affordable clothing and accessories for a mass-market audience.
  • D. Zara
    Zara is a town and district in Turkey known for its location in the eastern part of the Central Anatolia region.
  • E. H&M
    H&M is a global fast-fashion retail chain known for offering trendy clothing and accessories at affordable prices.
  • 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: Zara
Triple: [Milan Metro Line 3, station, Zara]
Generated description
Zara is a station on Milan’s Metro Line 3, serving as a public transportation stop within the city’s underground network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zara
Target entity description: Zara is a station on Milan’s Metro Line 3, serving as a public transportation stop within the city’s underground network.
  • A. Zara
    Zara is the historical Italian name for the coastal Croatian city of Zadar on the Adriatic Sea.
  • B. Zara
    Zara is a character in the 1953 film noir "Pickup on South Street," involved in the story’s underworld of espionage and crime.
  • C. Zara
    Zara is a global fast-fashion retail brand known for rapidly translating runway trends into affordable clothing and accessories for a mass-market audience.
  • D. Zara
    Zara is a town and district in Turkey known for its location in the eastern part of the Central Anatolia region.
  • E. H&M
    H&M is a global fast-fashion retail chain known for offering trendy clothing and accessories at affordable prices.
  • 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_69d85a1849f48190bf898068b2806fae completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03ec31f4881908b26ff7c381d7bc9 completed April 16, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a827d9081909fabc48bc685ba5b completed May 9, 2026, 11:29 a.m.
NEDg Description generation batch_69ff1b4c13e08190b2ccee59da02d0ae completed May 9, 2026, 11:32 a.m.
NED2 Entity disambiguation (via description) batch_69ff1bde8914819087d5d2ac88de34aa completed May 9, 2026, 11:34 a.m.
Created at: April 10, 2026, 3:21 a.m.