Triple
T21950413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jule Gregory Charney |
E542051
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jule |
—
|
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: Jule | Statement: [Jule Gregory Charney, givenName, Jule]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jule Context triple: [Jule Gregory Charney, givenName, Jule]
-
A.
Jule
chosen
Jule is a given name most notably associated with Jule Gregory Charney, a pioneering American meteorologist and one of the founders of modern numerical weather prediction.
-
B.
Jula
Jula is a major Mande language widely used as a trade and lingua franca in parts of West Africa, particularly in Burkina Faso, Côte d’Ivoire, and Mali.
-
C.
Julanne
Julanne is a feminine given name most notably borne by American silent film actress Julanne Johnston.
-
D.
Jole
Jole is a diminutive form of the male given name Jovan, commonly used in South Slavic languages as an affectionate or familiar nickname.
-
E.
Julin
Julin is a legendary medieval Baltic port city often identified with the Viking stronghold of Jomsborg in historical and saga traditions.
- 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_69e0c47ef0e48190a50e1bcc43f4b3fd |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1243bb9c88190a3774b9fa2af9871 |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:58 p.m.