Triple
T2285225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiffany |
E51374
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Tiffanie |
E252327
|
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: Tiffanie | Statement: [Tiffany, hasVariant, Tiffanie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tiffanie Context triple: [Tiffany, hasVariant, Tiffanie]
-
A.
Tiffani
chosen
Tiffani is a given name, typically a modern variant of the name Tiffany used for girls.
-
B.
Tina
Tina is the nickname of Tina Fey, an American comedian, writer, actress, and producer best known for her work on Saturday Night Live and 30 Rock.
-
C.
Tina
Tina, formally known as Baroness Stowell of Beeston, is a British Conservative politician and life peer in the House of Lords.
-
D.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
E.
Tamsin
Tamsin is a feminine given name of English origin, often associated with actresses and public figures such as Tamsin Egerton.
- 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_69a88b08e4308190bdac9aebcca1c91a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc245dd208190b13c5f5d05aa6990 |
completed | March 7, 2026, 6:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae894f9ff881909d1b3a7956d82576 |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:48 p.m.