Triple
T11776808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vanessa Stephen |
E280038
|
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 Stephen, givenName, Vanessa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vanessa Context triple: [Vanessa Stephen, givenName, Vanessa]
-
A.
Vanessa
Vanessa is the enigmatic, emotionally complex woman at the center of the film "By the Sea," whose inner turmoil drives the story’s exploration of marriage and personal grief.
-
B.
Vanessa
chosen
Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
-
C.
Vanessa
Vanessa is a fictional character associated with the setting of a beauty shop, likely depicted as someone involved in or frequenting the salon environment.
-
D.
Vanessa Black
Vanessa Black is a chef and television personality known for her culinary work and for being married to dancer and chef Dean Sheremet.
-
E.
Viviane
Viviane is a legendary enchantress of Arthurian romance, often identified as the Lady of the Lake and known for her role in mentoring and imprisoning the wizard Merlin.
- 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_69d6ab01d2688190ad8ed6bda487eaa5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a560bf548190afab3ad14f953a71 |
completed | April 10, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f090a95a908190a99e579e51cbeb4a |
completed | April 28, 2026, 10:49 a.m. |
Created at: April 8, 2026, 9:42 p.m.