Triple
T1787820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ava DuVernay |
E39428
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ava |
E183843
|
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: Ava | Statement: [Ava DuVernay, givenName, Ava]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ava Context triple: [Ava DuVernay, givenName, Ava]
-
A.
Ava
chosen
Ava is a feminine given name most famously associated with American actress and Hollywood icon Ava Gardner.
-
B.
Lena
Lena is an alternate given name of Lee Krasner, the influential American abstract expressionist painter and wife of Jackson Pollock.
-
C.
Lena
Lena is a common feminine given name used in many languages, often derived from longer names such as Magdalena or Helena.
-
D.
Emmie
Emmie is a diminutive given name, typically used as a affectionate or informal variant of names like Emma.
-
E.
Mia
Mia is a major fine art museum in Minneapolis, Minnesota, known for its extensive and diverse collection spanning thousands of years and cultures.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa650fd3448190a6a2c979db982cae |
completed | March 6, 2026, 5:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf52266081909b7478d76664d387 |
completed | March 8, 2026, 6:26 p.m. |
Created at: March 4, 2026, 7:32 p.m.