Triple
T3298396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zola |
E69270
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Zola (given name) |
E163638
|
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: Zola (given name) | Statement: [Zola, hasNotableBearer, Zola (given name)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zola (given name) Context triple: [Zola, hasNotableBearer, Zola (given name)]
-
A.
Zola
Zola is a French surname most famously borne by Émile Zola, the influential 19th-century novelist and leading figure of literary naturalism.
-
B.
Zola
Zola is a 2020 dark comedy-drama film based on a viral Twitter thread, following a Detroit waitress on a chaotic road trip into the world of stripping and crime.
-
C.
Zola
chosen
Zola is a township neighborhood in Soweto, South Africa, known for its vibrant street culture and significant role in the country’s urban history.
-
D.
Eugène
Eugène is a masculine given name of French origin, derived from the Greek "Eugenios," meaning "well-born" or "noble."
-
E.
Honoré
Honoré is the given name of the renowned 19th-century French novelist and playwright Honoré de Balzac.
- 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_69ad859e529c8190a404273f53cb487d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0a2f4708190821edb9700f62d2f |
completed | March 8, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f3d759908190b1f5170930ff03c5 |
completed | March 12, 2026, 5:11 p.m. |
Created at: March 8, 2026, 3:10 p.m.