Triple

T26611577

Position	Surface form	Disambiguated ID	Type / Status
Subject	Bagerhat District	`E667937`	entity
Predicate	contains	`P35`	FINISHED
Object	Mosque City of Bagerhat The Mosque City of Bagerhat is a UNESCO World Heritage Site in southwestern Bangladesh renowned for its extensive complex of 15th-century Islamic brick mosques and historic structures.	`E1732171`	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: Mosque City of Bagerhat | Statement: [Bagerhat District, contains, Mosque City of Bagerhat]

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: Mosque City of Bagerhat
Triple: [Bagerhat District, contains, Mosque City of Bagerhat]

Generated description

The Mosque City of Bagerhat is a UNESCO World Heritage Site in southwestern Bangladesh renowned for its extensive complex of 15th-century Islamic brick mosques and historic structures.

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_69ee9cfe16088190a3dddd68e3c7b1ea`	completed	April 26, 2026, 11:17 p.m.
NER	Named-entity recognition	`batch_69f615a93e3c8190a569c4d548da9900`	completed	May 2, 2026, 3:18 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a11c8479aa48190bde51181cb2a9fa1`	completed	May 23, 2026, 3:31 p.m.
NEDg	Description generation	`batch_6a11c8f290bc8190bfa1990ee7119516`	completed	May 23, 2026, 3:34 p.m.
NED2	Entity disambiguation (via description)	`batch_6a11ca6f162c8190a8c7fbc1e188ea90`	completed	May 23, 2026, 3:40 p.m.

Created at: April 27, 2026, 2:16 a.m.