Triple
T21947870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diane Venora |
E541979
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Diane |
—
|
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: Diane | Statement: [Diane Venora, givenName, Diane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diane Context triple: [Diane Venora, givenName, Diane]
-
A.
Diane
chosen
Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
-
B.
Dianne
Dianne is a feminine given name commonly used in English-speaking countries, often associated with the Roman goddess Diana and borne by various notable figures.
-
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.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
- 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_69f1243a2f788190bd4625fa79888696 |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:57 p.m.