Triple
T6725239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Diana Mayo |
E153499
|
entity |
| Predicate | givenName |
P17
|
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: [Lady Diana Mayo, givenName, Diana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diana Context triple: [Lady Diana Mayo, givenName, 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.
Diana
Diana is a renowned sculpture by Brazilian-Italian modernist artist Victor Brecheret, exemplifying his stylized, classical approach to the human figure.
-
C.
Melina
Melina is a key resistance fighter and love interest in the science fiction film "Total Recall," known for aiding the protagonist in his struggle against a corrupt Martian regime.
-
D.
Diane
Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
-
E.
Alessandra
Alessandra is an Italian politician, former actress, and granddaughter of Benito Mussolini.
- 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_69c6880afb988190ad88011b48ecfcba |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d14fb1a8819083287ca8bb576bba |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70afcbe308190909fe8f34ca8e5e2 |
completed | March 27, 2026, 10:55 p.m. |
Created at: March 27, 2026, 2:08 p.m.