Triple
T13483359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zora Folley |
E318429
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Zora |
E62792
|
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: Zora | Statement: [Zora Folley, givenName, Zora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zora Context triple: [Zora Folley, givenName, Zora]
-
A.
Zora
chosen
Zora is a feminine given name most famously associated with the African-American author and anthropologist Zora Neale Hurston.
-
B.
Kaska
Kaska is an Athabaskan Indigenous language traditionally spoken by the Kaska Dena people of northern Canada, primarily in the Yukon and northern British Columbia.
-
C.
Nez
Nez is a music video director known for directing Bruno Mars' hit video "The Lazy Song."
-
D.
Kaliko
Kaliko is an alternative name for the Keliko language, a Central Sudanic language spoken by the Keliko people of South Sudan and neighboring regions.
-
E.
Katisha
Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
- 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf3868ec8190a6a1803018d4f2d8 |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7463715dc8190a70a17b3ea661006 |
completed | May 3, 2026, 12:57 p.m. |
Created at: April 9, 2026, 9:42 p.m.