Triple
T5000563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lilaea |
E112361
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | nymph Lilaia |
E254061
|
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: nymph Lilaia | Statement: [Lilaea, associatedWith, nymph Lilaia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: nymph Lilaia Context triple: [Lilaea, associatedWith, nymph Lilaia]
-
A.
Nymfaio
Nymfaio is a traditional mountain village in northern Greece known for its preserved stone architecture, natural beauty, and cultural heritage.
-
B.
Nymphs
chosen
Nymphs are minor female nature deities in Greek mythology, typically depicted as beautiful young maidens associated with natural features like forests, rivers, mountains, and trees.
-
C.
Daphne
Daphne is a nymph from Greek mythology best known for being pursued by Apollo and transformed into a laurel tree to escape him.
-
D.
Daphne
Daphne is a coastal city in Baldwin County, Alabama, situated along the eastern shore of Mobile Bay.
-
E.
Daphne
Daphne is an HTTP, HTTP/2, and WebSocket server for ASGI applications, commonly used to serve Django and other Python async web frameworks.
- 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_69bd4432b32c81909f3b3c6bd10f0653 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd72bd90948190bf6ca21237402949 |
completed | March 20, 2026, 4:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be92557c508190a8c27b974906999f |
completed | March 21, 2026, 12:43 p.m. |
Created at: March 20, 2026, 1:34 p.m.