Triple

T10780680
Position Surface form Disambiguated ID Type / Status
Subject Thivim railway station E254310 entity
Predicate serves P98 FINISHED
Object Mapusa
Mapusa is a bustling commercial town in North Goa, India, known as a major market and transport hub near the popular beaches of the state.
 E886372 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: Mapusa | Statement: [Thivim railway station, serves, Mapusa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mapusa
Context triple: [Thivim railway station, serves, Mapusa]
  • A. Mouraria
    Mouraria is a historic Lisbon neighborhood known for its multicultural character, narrow medieval streets, and deep ties to traditional fado music.
  • B. Morrumbene
    Morrumbene is a small town in southern Mozambique known for its rural character within Inhambane Province.
  • C. Campomoro
    Campomoro is a small coastal village and seaside resort on the southwest coast of Corsica, known for its scenic bay and historic Genoese tower.
  • D. Makarora
    Makarora is a small rural settlement in New Zealand’s South Island, known as a gateway to outdoor activities and hiking in the Southern Alps region.
  • E. Mazabuka
    Mazabuka is a town in southern Zambia known for its sugar industry and agricultural production.
  • 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: Mapusa
Triple: [Thivim railway station, serves, Mapusa]
Generated description
Mapusa is a bustling commercial town in North Goa, India, known as a major market and transport hub near the popular beaches of the state.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mapusa
Target entity description: Mapusa is a bustling commercial town in North Goa, India, known as a major market and transport hub near the popular beaches of the state.
  • A. Mouraria
    Mouraria is a historic Lisbon neighborhood known for its multicultural character, narrow medieval streets, and deep ties to traditional fado music.
  • B. Morrumbene
    Morrumbene is a small town in southern Mozambique known for its rural character within Inhambane Province.
  • C. Campomoro
    Campomoro is a small coastal village and seaside resort on the southwest coast of Corsica, known for its scenic bay and historic Genoese tower.
  • D. Makarora
    Makarora is a small rural settlement in New Zealand’s South Island, known as a gateway to outdoor activities and hiking in the Southern Alps region.
  • E. Mazabuka
    Mazabuka is a town in southern Zambia known for its sugar industry and agricultural production.
  • 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_69d6aa609f008190a294200aefcb7bd5 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d732c48c488190a2b3162202b74726 completed April 9, 2026, 5:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69de55fbfc70819098eb40cf0d1b9e8c completed April 14, 2026, 2:58 p.m.
NEDg Description generation batch_69de5eacae148190b7ca2da87427572e completed April 14, 2026, 3:35 p.m.
NED2 Entity disambiguation (via description) batch_69de6397ff688190b6788489895a5360 completed April 14, 2026, 3:56 p.m.
Created at: April 8, 2026, 9:17 p.m.