Triple

T27722210
Position Surface form Disambiguated ID Type / Status
Subject Landgrave of Thuringia E698987 entity
Predicate precededBy P97 FINISHED
Object Counts of Thuringia
The Counts of Thuringia were medieval noble rulers who governed the Thuringian region in central Germany before the establishment of the Landgraviate of Thuringia.
E1787081 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: Counts of Thuringia | Statement: [Landgrave of Thuringia, precededBy, Counts of Thuringia]
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: Counts of Thuringia
Triple: [Landgrave of Thuringia, precededBy, Counts of Thuringia]
Generated description
The Counts of Thuringia were medieval noble rulers who governed the Thuringian region in central Germany before the establishment of the Landgraviate of Thuringia.

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_69ef591012dc8190a6f1ec994f9f7ff7 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f6363d2d608190a1128b7326403028 completed May 2, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12e46ae9f0819084c73e797b7c1060 completed May 24, 2026, 11:43 a.m.
NEDg Description generation batch_6a12e5ee8f048190b98a89d0023e1eba completed May 24, 2026, 11:50 a.m.
NED2 Entity disambiguation (via description) batch_6a12e695c82c81908646433dbbe85bfc completed May 24, 2026, 11:52 a.m.
Created at: April 27, 2026, 3:07 p.m.