Triple
T20750963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michaela Paetsch |
E510714
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Michaela |
—
|
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: Michaela | Statement: [Michaela Paetsch, givenName, Michaela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michaela Context triple: [Michaela Paetsch, givenName, Michaela]
-
A.
Michaela
chosen
Michaela is a feminine given name used in various languages, often considered the female form of Michael.
-
B.
Michela
Michela is a given name, commonly used in Italian- and Portuguese-speaking countries as a feminine form of Michael.
-
C.
Lucia
Lucia is a feminine given name of Latin origin, commonly associated with light and used in various European cultures.
-
D.
Valeria
Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
-
E.
Valeria
Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
- 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_69e0b4c845e88190b4c5f3ae79291182 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c22ad0c8819087a8db2bac8dd408 |
completed | April 21, 2026, 12:17 a.m. |
Created at: April 16, 2026, 12:34 p.m.