Triple
T3401738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diana |
E71669
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Dianna |
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: Dianna | Statement: [Diana, hasVariant, Dianna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dianna Context triple: [Diana, hasVariant, Dianna]
-
A.
Diane
Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
-
B.
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.
-
C.
Donna
Donna is a feminine given name of Italian origin that has been widely used in English-speaking countries.
-
D.
Adrienne
Adrienne is a feminine given name of French origin, commonly used in English- and French-speaking countries.
-
E.
Julianna
Julianna is a feminine given name most notably borne by American actress Julianna Margulies.
- 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_69ad85aac4808190a092c9cc8911f584 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb8c96d7c8190a1f9d035996f79e3 |
completed | March 8, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3739e9c588190b04b041564c1272d |
completed | March 13, 2026, 2:17 a.m. |
Created at: March 8, 2026, 3:14 p.m.