Triple
T19070766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daphni |
E466784
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Daphne (mythology) |
—
|
NE NERFINISHED |
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: Daphne (mythology) | Statement: [Daphni, namedAfter, Daphne (mythology)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daphne (mythology) Context triple: [Daphni, namedAfter, Daphne (mythology)]
-
A.
Daphne
chosen
Daphne is a nymph from Greek mythology best known for being pursued by Apollo and transformed into a laurel tree to escape him.
-
B.
Daphne
Daphne is a coastal city in Baldwin County, Alabama, situated along the eastern shore of Mobile Bay.
-
C.
Daphne
Daphne is an HTTP, HTTP/2, and WebSocket server for ASGI applications, commonly used to serve Django and other Python async web frameworks.
-
D.
Daphne
Daphne is the nickname of Dorothy de Sélincourt, an English editor and the wife of author A. A. Milne.
-
E.
Daphne
Daphne is a 2017 British drama film starring Emily Beecham as a sharp-tongued, disaffected Londoner whose life begins to unravel after she witnesses a violent incident.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd04f4488190b1121cc53ef2bfd6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e19ea01c8190b9bb789da32f9a90 |
completed | April 20, 2026, 8:19 a.m. |
Created at: April 10, 2026, 12:04 p.m.