Triple
T2577746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vanessa Bell |
E57015
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Vanessa |
E116721
|
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: Vanessa | Statement: [Vanessa Bell, givenName, Vanessa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vanessa Context triple: [Vanessa Bell, givenName, Vanessa]
-
A.
Vanessa
chosen
Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
-
B.
Nicole
Nicole is a central character in Margaret Atwood's dystopian novel "The Testaments," whose story helps expose and challenge the oppressive regime of Gilead.
-
C.
Vivian
Vivian "Buster" Burey Marshall was a civil rights activist and the first wife of U.S. Supreme Court Justice Thurgood Marshall.
-
D.
Danielle
"Danielle" is a work created by Sarah Churchill, known as part of her contributions to the arts.
-
E.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
- 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_69ab4a4dca6481908c301f8e317396e7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3a73a508190bf12e889a5d4bbf3 |
completed | March 7, 2026, 7:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af6576a0a8819080d9241801675b19 |
completed | March 10, 2026, 12:27 a.m. |
Created at: March 6, 2026, 9:49 p.m.