Triple

T10742603
Position Surface form Disambiguated ID Type / Status
Subject Collblanc E253364 entity
Predicate metroLine P848 FINISHED
Object L10 Sud
L10 Sud is a line of the Barcelona Metro network serving the southern metropolitan area with automated, driverless trains.
 E883866 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: L10 Sud | Statement: [Collblanc, metroLine, L10 Sud]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: L10 Sud
Context triple: [Collblanc, metroLine, L10 Sud]
  • A. Skudai Highway
    Skudai Highway is a major roadway in Johor, Malaysia, that serves as a key route connecting the town of Skudai with other parts of the region.
  • B. LP-3 road
    LP-3 road is a regional roadway that connects to the city of El Paso, serving as part of its surrounding transport network.
  • C. Fujin Road
    Fujin Road is a metro station in Shanghai, China, serving as the northern terminus of Line 1 of the Shanghai Metro system.
  • D. MA-10 road
    The MA-10 road is a scenic mountain route that winds along Mallorca’s Serra de Tramuntana, renowned for its dramatic coastal views and access to picturesque villages.
  • E. Julu Road
    Julu Road is a historic, tree-lined street in Shanghai known for its blend of old lane houses, trendy cafes, and boutiques in the former French Concession area.
  • 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: L10 Sud
Triple: [Collblanc, metroLine, L10 Sud]
Generated description
L10 Sud is a line of the Barcelona Metro network serving the southern metropolitan area with automated, driverless trains.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: L10 Sud
Target entity description: L10 Sud is a line of the Barcelona Metro network serving the southern metropolitan area with automated, driverless trains.
  • A. Skudai Highway
    Skudai Highway is a major roadway in Johor, Malaysia, that serves as a key route connecting the town of Skudai with other parts of the region.
  • B. LP-3 road
    LP-3 road is a regional roadway that connects to the city of El Paso, serving as part of its surrounding transport network.
  • C. Fujin Road
    Fujin Road is a metro station in Shanghai, China, serving as the northern terminus of Line 1 of the Shanghai Metro system.
  • D. MA-10 road
    The MA-10 road is a scenic mountain route that winds along Mallorca’s Serra de Tramuntana, renowned for its dramatic coastal views and access to picturesque villages.
  • E. Julu Road
    Julu Road is a historic, tree-lined street in Shanghai known for its blend of old lane houses, trendy cafes, and boutiques in the former French Concession area.
  • 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_69d6aa5e51e8819095f06881cecf152e completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d710456ec88190ad8aff8804d13aa9 completed April 9, 2026, 2:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69de22fc13b0819098caf88328397053 completed April 14, 2026, 11:20 a.m.
NEDg Description generation batch_69de271e2698819093bba748a0a0db5d completed April 14, 2026, 11:38 a.m.
NED2 Entity disambiguation (via description) batch_69de2cdd79608190bad8045939556bc7 completed April 14, 2026, 12:02 p.m.
Created at: April 8, 2026, 9:15 p.m.