Triple

T29687617
Position Surface form Disambiguated ID Type / Status
Subject Imagina of Isenburg-Limburg E751129 entity
Predicate child P120 FINISHED
Object Matilda of Nassau
Matilda of Nassau was a medieval noblewoman of the House of Nassau, known as the daughter of Count Henry II of Nassau and the wife of Count Gerlach I of Nassau.
E1921944 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: Matilda of Nassau | Statement: [Imagina of Isenburg-Limburg, child, Matilda of Nassau]
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: Matilda of Nassau
Triple: [Imagina of Isenburg-Limburg, child, Matilda of Nassau]
Generated description
Matilda of Nassau was a medieval noblewoman of the House of Nassau, known as the daughter of Count Henry II of Nassau and the wife of Count Gerlach I of Nassau.

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_69f0d625b09481909b0b69aea1e846c8 completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f6729193908190b7a3e27a13fca854 completed May 2, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2856d3fc148190a378c79cf9ab7ff6 completed June 9, 2026, 6:09 p.m.
NEDg Description generation batch_6a2859cac4a48190bb279ab2c4e933a9 completed June 9, 2026, 6:22 p.m.
NED2 Entity disambiguation (via description) batch_6a285a60386081909c73d1ef55aaeb8a completed June 9, 2026, 6:24 p.m.
Created at: April 28, 2026, 7:14 p.m.