Triple

T25128571
Position Surface form Disambiguated ID Type / Status
Subject M5 (Bucharest Metro) E629460 entity
Predicate hasStation P35 FINISHED
Object Tudor Vladimirescu (Bucharest Metro) station
Tudor Vladimirescu is a Bucharest Metro station on line M5 serving the surrounding residential and commercial areas in Romania’s capital.
E1692711 NE FINISHED

How this triple was built (2 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: Tudor Vladimirescu (Bucharest Metro) station | Statement: [M5 (Bucharest Metro), hasStation, Tudor Vladimirescu (Bucharest Metro) station]
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: Tudor Vladimirescu (Bucharest Metro) station
Triple: [M5 (Bucharest Metro), hasStation, Tudor Vladimirescu (Bucharest Metro) station]
Generated description
Tudor Vladimirescu is a Bucharest Metro station on line M5 serving the surrounding residential and commercial areas in Romania’s capital.

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_69e2ff3288048190bd82c3b7f7bd0e62 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f465f5dac08190a76de4990c496bf6 completed May 1, 2026, 8:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10cbc51490819092df1bf41843a82a completed May 22, 2026, 9:33 p.m.
NEDg Description generation batch_6a10cc9e0d7c81909e6acbbc8c7ac7de completed May 22, 2026, 9:37 p.m.
NED2 Entity disambiguation (via description) batch_6a10cd2723f88190a55aea01fba6dcad completed May 22, 2026, 9:39 p.m.
Created at: April 18, 2026, 6:28 a.m.