Triple
T15151216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vanessa Hudgens |
E361945
|
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 Hudgens, givenName, Vanessa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vanessa Context triple: [Vanessa Hudgens, 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
Vanessa is a fictional character associated with the setting of a beauty shop, likely depicted as someone involved in or frequenting the salon environment.
-
C.
Vanessa
Vanessa is a central character in the sitcom "Grandfathered," known for her close connection to the protagonist and involvement in the show's family-centered storylines.
-
D.
Vanessa
chosen
Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
-
E.
Vanessa
Vanessa is a minor character from the TV sitcom "Seinfeld," appearing as a romantic interest of Jerry in early episodes.
- 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_69d85a0759908190b8a051d2e2a1cbe6 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00609040081908c849475a2fa6443 |
completed | April 15, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec88419108190860319a9bcab1eef |
completed | May 9, 2026, 5:39 a.m. |
Created at: April 10, 2026, 3:07 a.m.