Triple

T26892545
Position Surface form Disambiguated ID Type / Status
Subject Bangladesh Railway E677813 entity
Predicate hasDivision P35 FINISHED
Object Pakshi Railway Division
Pakshi Railway Division is an administrative division of Bangladesh Railway responsible for managing railway operations, infrastructure, and services in its designated region of the country.
E1764266 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: Pakshi Railway Division | Statement: [Bangladesh Railway, hasDivision, Pakshi Railway Division]
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: Pakshi Railway Division
Triple: [Bangladesh Railway, hasDivision, Pakshi Railway Division]
Generated description
Pakshi Railway Division is an administrative division of Bangladesh Railway responsible for managing railway operations, infrastructure, and services in its designated region of the country.

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_69eee9befee48190a26f214faa867be7 completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61f69e4508190ab20c3f2052282e7 completed May 2, 2026, 3:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12624dea88819093dd0543a32d9d92 completed May 24, 2026, 2:28 a.m.
NEDg Description generation batch_6a126fe800308190873e7804d76f75ef completed May 24, 2026, 3:26 a.m.
NED2 Entity disambiguation (via description) batch_6a1270508ed08190b282ab7dabdea569 completed May 24, 2026, 3:28 a.m.
Created at: April 27, 2026, 5:46 a.m.