Triple
T7298083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daphne du Maurier |
E164574
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Daphne |
E88548
|
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: Daphne | Statement: [Daphne du Maurier, givenName, Daphne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daphne Context triple: [Daphne du Maurier, givenName, Daphne]
-
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 a fast, swinging jazz composition by guitarist Django Reinhardt that has become a recognized standard in the gypsy jazz repertoire.
-
D.
Daphne
Daphne is an HTTP, HTTP/2, and WebSocket server for ASGI applications, commonly used to serve Django and other Python async web frameworks.
-
E.
Belphoebe
Belphoebe is a chaste and martial huntress in Edmund Spenser’s epic poem *The Faerie Queene*, often interpreted as an idealized representation of Queen Elizabeth I.
- 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_69c6887a499881909dd23341399c59d8 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6eb8f83c881909e8eae85410f9659 |
completed | March 27, 2026, 8:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7e54c25c88190891311b72f242e86 |
completed | March 28, 2026, 2:27 p.m. |
Created at: March 27, 2026, 3 p.m.