Triple

T28822074
Position Surface form Disambiguated ID Type / Status
Subject Charlottenplatz E727794 entity
Predicate namedAfter P63 FINISHED
Object Charlotte of Württemberg
Charlotte of Württemberg was a 19th-century German princess from the House of Württemberg who became Empress consort of Mexico as the wife of Emperor Maximilian I.
E2294565 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: Charlotte of Württemberg | Statement: [Charlottenplatz, namedAfter, Charlotte of Württemberg]
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: Charlotte of Württemberg
Triple: [Charlottenplatz, namedAfter, Charlotte of Württemberg]
Generated description
Charlotte of Württemberg was a 19th-century German princess from the House of Württemberg who became Empress consort of Mexico as the wife of Emperor Maximilian I.

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_69f0319d09088190bbf14cdf1987792a completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f659355a208190be2609ffc7a9c427 completed May 2, 2026, 8:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7bfd153f1081908b74d12ecc81c2ca completed Aug. 12, 2026, 4:56 a.m.
NEDg Description generation batch_6a7bfde3b03481909d6dc5db112d5575 completed Aug. 12, 2026, 5 a.m.
NED2 Entity disambiguation (via description) batch_6a7bfe5cc24c819090d3b26bd185bc36 completed Aug. 12, 2026, 5:02 a.m.
Created at: April 28, 2026, 6:34 a.m.