Triple

T26894628
Position Surface form Disambiguated ID Type / Status
Subject Shah Beg Arghun E677866 entity
Predicate alsoKnownAs P39 FINISHED
Object Shah Beg
Shah Beg was a 16th-century Arghun ruler best known as the founder of the Arghun dynasty’s rule over Sindh in present-day Pakistan.
E1752562 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: Shah Beg | Statement: [Shah Beg Arghun, alsoKnownAs, Shah Beg]
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: Shah Beg
Triple: [Shah Beg Arghun, alsoKnownAs, Shah Beg]
Generated description
Shah Beg was a 16th-century Arghun ruler best known as the founder of the Arghun dynasty’s rule over Sindh in present-day Pakistan.

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_69f61fa9a1ec8190bc4063656815a1c5 completed May 2, 2026, 4 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12298e3ee4819082182b6e9864d13f completed May 23, 2026, 10:26 p.m.
NEDg Description generation batch_6a122ab3c688819090346bce8a20c061 completed May 23, 2026, 10:31 p.m.
NED2 Entity disambiguation (via description) batch_6a122bf0a15c81909e479281bb9e7d73 completed May 23, 2026, 10:36 p.m.
Created at: April 27, 2026, 5:47 a.m.