Triple
T14834017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisa Davis |
E348781
|
entity |
| Predicate | voiceRole |
P12691
|
FINISHED |
| Object | Anita |
E162140
|
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: Anita | Statement: [Lisa Davis, voiceRole, Anita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anita Context triple: [Lisa Davis, voiceRole, Anita]
-
A.
Anita
chosen
Anita is a feminine given name used in various cultures, often as a diminutive of names like Ana or Anna.
-
B.
Marita
Marita is a feminine given name commonly used as a diminutive or affectionate form of the name Marie in various European languages.
-
C.
Anita Gregory
Anita Gregory is a skeptical paranormal investigator character in "The Conjuring 2," loosely inspired by real-life parapsychologist Anita Gregory.
-
D.
Janette
Janette is a feminine given name of English origin, often considered a diminutive or variant of Janet or Jane.
-
E.
Benita
Benita is a feminine given name of Latin origin, often associated with the meaning "blessed."
- 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded075af0881908fb35a9e7ee46749 |
completed | April 14, 2026, 11:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe38a5d8888190821988ad00351d05 |
completed | May 8, 2026, 7:25 p.m. |
Created at: April 10, 2026, 1:52 a.m.