Triple
T9999397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dana Brunetti |
E197284
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Dana |
E135363
|
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: Dana | Statement: [Dana Brunetti, givenName, Dana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dana Context triple: [Dana Brunetti, givenName, Dana]
-
A.
Dana
chosen
Dana is a feminine given name commonly used in various cultures, including Czech, English, and Hebrew-speaking communities.
-
B.
Dana
Dana is a scientific work or authority that provides the formal description and classification of Antarctic krill.
-
C.
The Dana
The Dana is the historic former Shrewsbury Prison in Shropshire, England, now known for guided tours and heritage events.
-
D.
Tanasi
Tanasi was a historic Overhill Cherokee town in present-day Tennessee that gave the state its name and served as an early political center for the Cherokee.
-
E.
Qualley
Qualley is the surname of an American family best known for actress and model Margaret Qualley and her mother, actress Andie MacDowell.
- 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcc8dc9c081909b6d20909ada09cf |
completed | April 2, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2584bd6cc8190841353847dd2f00c |
completed | April 5, 2026, 12:40 p.m. |
Created at: March 30, 2026, 8:51 p.m.