Triple
T3211517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dana Delany |
E67291
|
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 Delany, givenName, Dana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dana Context triple: [Dana Delany, givenName, Dana]
-
A.
Dana
Dana is a scientific work or authority that provides the formal description and classification of Antarctic krill.
-
B.
Dana
chosen
Dana is a feminine given name commonly used in various cultures, including Czech, English, and Hebrew-speaking communities.
-
C.
Niles
Niles is a historic former town in California, now a district of Fremont, known for its early silent film industry and railroad heritage.
-
D.
Son
Son is a Japanese surname most prominently associated with Masayoshi Son, the billionaire founder and CEO of SoftBank.
-
E.
Osnos
Osnos is a surname most notably associated with American journalist and author Peter Osnos and his family.
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaaba224c8190ad2f4e0ed1c2ca4a |
completed | March 8, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b262326d508190a4a702ed549fed99 |
completed | March 12, 2026, 6:50 a.m. |
Created at: March 8, 2026, 3:07 p.m.