Triple
T26064085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna de’ Medici |
E657350
|
entity |
| Predicate | motherOf |
P120
|
FINISHED |
| Object |
Empress Claudia Felicitas
Empress Claudia Felicitas was a 17th-century Holy Roman Empress, the second wife of Emperor Leopold I, known for her brief tenure and early death, which ended hopes for a lasting Tyrolean-Habsburg line through her.
|
E1708420
|
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: Empress Claudia Felicitas | Statement: [Anna de’ Medici, motherOf, Empress Claudia Felicitas]
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: Empress Claudia Felicitas Triple: [Anna de’ Medici, motherOf, Empress Claudia Felicitas]
Generated description
Empress Claudia Felicitas was a 17th-century Holy Roman Empress, the second wife of Emperor Leopold I, known for her brief tenure and early death, which ended hopes for a lasting Tyrolean-Habsburg line through her.
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_69ee5bbd788481909e22bd7153d0c037 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f606954f288190bcb77d432ed3617c |
completed | May 2, 2026, 2:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a111b2a098481909307f13643e81bf6 |
completed | May 23, 2026, 3:12 a.m. |
| NEDg | Description generation | batch_6a111cadcd9c819085ea0676070228ad |
completed | May 23, 2026, 3:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a111df51a8c8190841ee5f63b0c5633 |
completed | May 23, 2026, 3:24 a.m. |
Created at: April 26, 2026, 7:21 p.m.