Triple

T27118974
Position Surface form Disambiguated ID Type / Status
Subject Prince of Commercy E686935 entity
Predicate basedOn P98 FINISHED
Object Lordship of Commercy
The Lordship of Commercy was a medieval seigneurial domain in northeastern France that formed the territorial and feudal basis for the later princely title of Commercy.
 E1758552 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: Lordship of Commercy | Statement: [Prince of Commercy, basedOn, Lordship of Commercy]
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: Lordship of Commercy
Triple: [Prince of Commercy, basedOn, Lordship of Commercy]
Generated description
The Lordship of Commercy was a medieval seigneurial domain in northeastern France that formed the territorial and feudal basis for the later princely title of Commercy.

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_69ef148c2b588190afc15b529f7af845 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f624421d188190a5356a4596df536e completed May 2, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a124815a1148190a70fb328246235f7 completed May 24, 2026, 12:36 a.m.
NEDg Description generation batch_6a1249514a4881909357bb4e1c502d2b completed May 24, 2026, 12:41 a.m.
NED2 Entity disambiguation (via description) batch_6a124a0a69188190a543bca2b05b2402 completed May 24, 2026, 12:44 a.m.
Created at: April 27, 2026, 8:58 a.m.