Triple

T26653654
Position Surface form Disambiguated ID Type / Status
Subject Daut Pasha Hamam E666434 entity
Predicate namedAfter P63 FINISHED
Object Daut Pasha
Daut Pasha was an Ottoman statesman and high-ranking official after whom the historic Daut Pasha Hamam in Skopje is named.
E1794682 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: Daut Pasha | Statement: [Daut Pasha Hamam, namedAfter, Daut Pasha]
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: Daut Pasha
Triple: [Daut Pasha Hamam, namedAfter, Daut Pasha]
Generated description
Daut Pasha was an Ottoman statesman and high-ranking official after whom the historic Daut Pasha Hamam in Skopje is named.

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_69ee9cf8c7188190b9b00270a8a89164 completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f6167ccb308190a3183b2145bf4ce8 completed May 2, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a130320705c81908a413d19714206c9 completed May 24, 2026, 1:54 p.m.
NEDg Description generation batch_6a1304306b688190b128a526eea2486e completed May 24, 2026, 1:59 p.m.
NED2 Entity disambiguation (via description) batch_6a130625d7a48190a885048db3b29854 completed May 24, 2026, 2:07 p.m.
Created at: April 27, 2026, 2:34 a.m.