Triple
T2408827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ephesus |
E50337
|
entity |
| Predicate | associatedWithDeity |
P1481
|
FINISHED |
| Object | Diana |
E71669
|
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: Diana | Statement: [Ephesus, associatedWithDeity, Diana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diana Context triple: [Ephesus, associatedWithDeity, Diana]
-
A.
Diana
chosen
Diana is a feminine given name of Latin origin, famously borne by the Roman goddess of the hunt and by Diana, Princess of Wales.
-
B.
Diane
Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
-
C.
Anastasia
Anastasia is a 1956 historical drama film starring Ingrid Bergman as an amnesiac woman who may be the surviving daughter of Russia’s last tsar.
-
D.
Anastasia
Anastasia is a stage musical with a book by Terrence McNally that reimagines the legend of the lost Russian Grand Duchess through a sweeping, romantic historical narrative.
-
E.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
- 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_69a88b0339a88190a1207333cd271cc9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc925c6e481909bfd45b361d21963 |
completed | March 7, 2026, 6:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3edc63c8190ac6737bf28993f1b |
completed | March 9, 2026, 11:50 a.m. |
Created at: March 4, 2026, 7:58 p.m.