Triple
T21378219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elisabeth Amalie of Hesse-Darmstadt |
E527269
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Amalie |
—
|
NE NERFINISHED |
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: Amalie | Statement: [Elisabeth Amalie of Hesse-Darmstadt, givenName, Amalie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amalie Context triple: [Elisabeth Amalie of Hesse-Darmstadt, givenName, Amalie]
-
A.
Amalie
Amalie is a motor oil and lubricants brand known for producing automotive and industrial oils.
-
B.
Amalie
Amalie is a given name associated with Princess Marianne of Prussia, a 19th-century Prussian royal.
-
C.
Amalie
chosen
Amalie is a given name associated here with Antoinette Amalie of Brunswick-Wolfenbüttel, a German noblewoman from the House of Brunswick.
-
D.
Amalie
Amalie is the given first name of the pioneering German mathematician Emmy Noether, renowned for her foundational contributions to abstract algebra and theoretical physics.
-
E.
Amalia
Amalia is a novel by Finnish writer Sylvi Kekkonen, known for its introspective portrayal of women’s inner lives in mid-20th-century Finland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0ca5e1c81909b06958459c553dc |
completed | April 22, 2026, 11:28 a.m. |
Created at: April 16, 2026, 5:11 p.m.