Triple
T14159013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Belknap |
E350888
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Anna |
unclear NED1
|
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: Anna | Statement: [Anna Belknap, givenName, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Anna Belknap, givenName, Anna]
-
A.
Anna
Anna is the tragic, aristocratic heroine of Leo Tolstoy’s novel "Anna Karenina," whose passionate affair and struggle against societal norms lead to her downfall.
-
B.
Anna
Anna is a character appearing in the home-renovation reality TV series "Fixer Upper."
-
C.
Anna
Anna of Moscow was a medieval Russian noblewoman and princess associated with the ruling dynasties of Muscovy.
-
D.
Anna
Anna is the popular nickname of C. N. Annadurai, a prominent Indian politician, writer, and founder of the Dravida Munnetra Kazhagam (DMK) party who served as Chief Minister of Tamil Nadu.
-
E.
Anna
Anna is a central fictional character in Michael Ondaatje's novel "Divisadero," around whom much of the story's emotional and narrative complexity revolves.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61377de48190a3470d28f0edd34a |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7ef4d80819098d210503f5d22e9 |
completed | May 7, 2026, 8:37 p.m. |
Created at: April 10, 2026, 12:58 a.m.