Triple

T35572403
Position Surface form Disambiguated ID Type / Status
Subject Kudal railway station E1027974 entity
Predicate nearbyDistrictHeadquarters P44325 FINISHED
Object Oros (Sindhudurg district headquarters)
Oros is the administrative headquarters town of Sindhudurg district in Maharashtra, India.
 E2146376 NE FINISHED

How this triple was built (3 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: Oros (Sindhudurg district headquarters) | Statement: [Kudal railway station, nearbyDistrictHeadquarters, Oros (Sindhudurg district headquarters)]
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: Oros (Sindhudurg district headquarters)
Triple: [Kudal railway station, nearbyDistrictHeadquarters, Oros (Sindhudurg district headquarters)]
Generated description
Oros is the administrative headquarters town of Sindhudurg district in Maharashtra, India.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: nearbyDistrictHeadquarters
Context triple: [Kudal railway station, nearbyDistrictHeadquarters, Oros (Sindhudurg district headquarters)]
  • A. nearestDistrictHeadquarters
    Indicates that one location is the closest district headquarters to another specified place.
  • B. districtHeadquarterWithin
    Indicates that the administrative headquarters of a district is located within the boundaries of a specified area or entity.
  • C. locatedNearGovernmentDistrict
    Indicates that one entity is situated close to or in the immediate vicinity of a government district.
  • D. nearHeadquartersOf chosen
    Indicates that one entity is located geographically close to the headquarters of another entity.
  • E. districtHeadquartersLocation
    Indicates the place where the administrative headquarters of a district is located.
  • F. None of above.

Provenance (6 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_69f76e0386688190b931bacdc145938c completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69fec4cffed08190b5e5e7cc0c87493e completed May 9, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a38530442dc819084285eb44cc9a594 completed June 21, 2026, 9:09 p.m.
NEDg Description generation batch_6a385475674c8190866dd53e47dac3bd completed June 21, 2026, 9:15 p.m.
NED2 Entity disambiguation (via description) batch_6a38552e7974819082b7ee16b00a21d0 completed June 21, 2026, 9:18 p.m.
PD Predicate disambiguation batch_69fec2ea7fe08190bd751b39515f69d1 completed May 9, 2026, 5:15 a.m.
Created at: May 3, 2026, 4:04 p.m.