Triple
T6462216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Primavera |
E142147
|
entity |
| Predicate | depicts |
P1581
|
FINISHED |
| Object | Flora |
E201351
|
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: Flora | Statement: [Primavera, depicts, Flora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flora Context triple: [Primavera, depicts, Flora]
-
A.
Flora
chosen
Flora is a symbolist painting by Evelyn De Morgan depicting the Roman goddess of flowers and spring in a richly allegorical, Pre-Raphaelite-inspired style.
-
B.
Flora
Flora is the young niece in Henry James's novella "The Turn of the Screw," whose eerie innocence and ambiguous relationship to the supernatural are central to the story's psychological horror.
-
C.
Flora
Flora is the middle name of Ruth Disney, the daughter of Walt Disney and his wife Lillian.
-
D.
Flora
Flora is one of the three good fairies in Disney's "Sleeping Beauty," known for her red attire, leadership among the fairies, and role in protecting Princess Aurora.
-
E.
Petal
Petal is a U.S.-based fintech company that offers credit cards and builds credit for consumers using alternative underwriting methods that go beyond traditional credit scores.
- 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_69c008d2f91c8190a8178767a35e08fc |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c069f7e5908190ae4d8da2b14d274f |
completed | March 22, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64be4e6888190a685f447e9950163 |
completed | March 27, 2026, 9:20 a.m. |
Created at: March 22, 2026, 4:49 p.m.