Triple

T25128365
Position Surface form Disambiguated ID Type / Status
Subject Metrorex E629455 entity
Predicate operatesNetwork P5033 FINISHED
Object Bucharest Metro Line M1
Bucharest Metro Line M1 is one of the main lines of the Bucharest Metro system, forming a circular route that connects several key residential and industrial areas of Romania’s capital.
E1671263 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: Bucharest Metro Line M1 | Statement: [Metrorex, operatesNetwork, Bucharest Metro Line M1]
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: Bucharest Metro Line M1
Triple: [Metrorex, operatesNetwork, Bucharest Metro Line M1]
Generated description
Bucharest Metro Line M1 is one of the main lines of the Bucharest Metro system, forming a circular route that connects several key residential and industrial areas of 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_6a1067bd00a88190b6c599c6ebc9044e completed May 22, 2026, 2:27 p.m.
NEDg Description generation batch_6a10691de65881909d3438fe12074a4a completed May 22, 2026, 2:33 p.m.
NED2 Entity disambiguation (via description) batch_6a1069f2545c819087181f62e6ccbf3f completed May 22, 2026, 2:36 p.m.
Created at: April 18, 2026, 6:28 a.m.