Triple

T31311029
Position Surface form Disambiguated ID Type / Status
Subject Papyrus Anastasi I E798461 entity
Predicate namedAfter P63 FINISHED
Object Anastasi collection
The Anastasi collection is a renowned assemblage of ancient Egyptian papyri and manuscripts once owned by the 19th-century collector Giovanni Anastasi.
E1955693 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: Anastasi collection | Statement: [Papyrus Anastasi I, namedAfter, Anastasi collection]
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: Anastasi collection
Triple: [Papyrus Anastasi I, namedAfter, Anastasi collection]
Generated description
The Anastasi collection is a renowned assemblage of ancient Egyptian papyri and manuscripts once owned by the 19th-century collector Giovanni Anastasi.

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_69f224e1932c81908fef14f7b03a10b7 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69e68e69481909e7d8adb46dd50f5 completed May 3, 2026, 1:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2a1e41f8408190a5f3b295ef0fe93a completed June 11, 2026, 2:32 a.m.
NEDg Description generation batch_6a2a1fd31ed4819080327ebb2163badd completed June 11, 2026, 2:39 a.m.
NED2 Entity disambiguation (via description) batch_6a2a202e5db08190858d31f5b248d5f6 completed June 11, 2026, 2:40 a.m.
Created at: April 29, 2026, 9:15 p.m.