Triple

T23112611
Position Surface form Disambiguated ID Type / Status
Subject Northern State E576360 entity
Predicate containsArchaeologicalSite P11933 FINISHED
Object Sai Island
Sai Island is a historically significant Nile island in northern Sudan known for its rich archaeological remains from ancient Nubian and Egyptian civilizations.
E2290659 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: Sai Island | Statement: [Northern State, containsArchaeologicalSite, Sai Island]
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: Sai Island
Triple: [Northern State, containsArchaeologicalSite, Sai Island]
Generated description
Sai Island is a historically significant Nile island in northern Sudan known for its rich archaeological remains from ancient Nubian and Egyptian civilizations.

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_69e245f4af548190898d434a64a1e774 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18e100d408190a7349563235640d5 completed April 29, 2026, 4:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5bee1cbb0c8190b95ce1eaaa03234c completed July 18, 2026, 9:20 p.m.
NEDg Description generation batch_6a5bee83fbe481909f79bda60fa4efea completed July 18, 2026, 9:22 p.m.
NED2 Entity disambiguation (via description) batch_6a5bee9f128c819096acf96ae234670c completed July 18, 2026, 9:22 p.m.
Created at: April 17, 2026, 3:58 p.m.