Triple

T4302691
Position Surface form Disambiguated ID Type / Status
Subject Lampung E99876 entity
Predicate hasCity P316 FINISHED
Object Metro
Metro is a city in the Indonesian province of Lampung on the island of Sumatra, known as one of the region’s key urban and educational centers.
 E429360 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: Metro | Statement: [Lampung, hasCity, Metro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Metro
Context triple: [Lampung, hasCity, Metro]
  • A. Metro
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • B. Metro
    Metro is the primary public transportation agency serving Los Angeles County, operating buses, light rail, subway, and other transit services across the region.
  • C. Metro
    "Metro" is a Russian disaster thriller film featuring Svetlana Khodchenkova in a prominent role, centered on a catastrophic flood in the Moscow subway system.
  • D. Metro
    Metro is a multinational wholesale and food retail company headquartered in Germany, operating cash-and-carry stores and serving professional customers worldwide.
  • E. Metro
    Metro is the professional alias of Metro Boomin, a prominent American record producer and DJ known for shaping the sound of modern hip-hop and trap music.
  • 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: Metro
Triple: [Lampung, hasCity, Metro]
Generated description
Metro is a city in the Indonesian province of Lampung on the island of Sumatra, known as one of the region’s key urban and educational centers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Metro
Target entity description: Metro is a city in the Indonesian province of Lampung on the island of Sumatra, known as one of the region’s key urban and educational centers.
  • A. Metro
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • B. Metro
    Metro is the professional alias of Metro Boomin, a prominent American record producer and DJ known for shaping the sound of modern hip-hop and trap music.
  • C. Metro
    Metro is the Los Angeles Police Department’s elite Metropolitan Division, known for handling specialized tactical operations, crowd control, and high-risk incidents.
  • D. Metro
    Metro is a multinational wholesale and food retail company headquartered in Germany, operating cash-and-carry stores and serving professional customers worldwide.
  • E. Metro
    Metro is the primary public transportation agency serving Los Angeles County, operating buses, light rail, subway, and other transit services across the region.
  • 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_69b345528ebc8190b5abc7e95094792d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b350b66450819089c9ff6ff9f045e5 completed March 12, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c7507f1081909cf737dff00542d9 completed March 14, 2026, 8:38 p.m.
NEDg Description generation batch_69b5c909b7848190bbe00249e0c9e555 completed March 14, 2026, 8:46 p.m.
NED2 Entity disambiguation (via description) batch_69b5c97117088190972bba5dbc1553f7 completed March 14, 2026, 8:47 p.m.
Created at: March 12, 2026, 11:08 p.m.