Triple
T3812761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elena Propper de Callejón |
E93173
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Elena |
E86412
|
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: Elena | Statement: [Elena Propper de Callejón, givenName, Elena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elena Context triple: [Elena Propper de Callejón, givenName, Elena]
-
A.
Elena
chosen
Elena is a feminine given name of Greek origin, commonly used in many languages as a variant of Helen or Helena.
-
B.
Elena of Avalor
Elena of Avalor is an animated Disney television series following a brave Latina princess who rules the magical kingdom of Avalor.
-
C.
Natalya
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
-
D.
Valeria
Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69aed96a60088190ab1df8390fffc935 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aee8db8a288190afd1e3b9dcf02e97 |
completed | March 9, 2026, 3:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb3a69908190ba8e7ac37c8ca0f8 |
completed | March 14, 2026, 6:07 a.m. |
Created at: March 9, 2026, 3:16 p.m.