Triple

T26504450
Position Surface form Disambiguated ID Type / Status
Subject With Fire and Sword E669506 entity
Predicate mainCharacter P1183 FINISHED
Object Jan Skrzetuski
Jan Skrzetuski is a fictional Polish nobleman and soldier who serves as the valiant protagonist of Henryk Sienkiewicz’s historical novel "With Fire and Sword."
E1916474 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: Jan Skrzetuski | Statement: [With Fire and Sword, mainCharacter, Jan Skrzetuski]
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: Jan Skrzetuski
Triple: [With Fire and Sword, mainCharacter, Jan Skrzetuski]
Generated description
Jan Skrzetuski is a fictional Polish nobleman and soldier who serves as the valiant protagonist of Henryk Sienkiewicz’s historical novel "With Fire and Sword."

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_69eeb319ec70819090834c2591cf5f1e completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f6138cc2d48190b30f14187b853bf3 completed May 2, 2026, 3:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27abf2b6548190a83d020ed3856859 completed June 9, 2026, 6 a.m.
NEDg Description generation batch_6a27acae789081908a0500ce5b46b481 completed June 9, 2026, 6:03 a.m.
NED2 Entity disambiguation (via description) batch_6a27ad6a946c8190a4d6aafcb235849d completed June 9, 2026, 6:06 a.m.
Created at: April 27, 2026, 1:15 a.m.