Triple
T18942212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pomona |
E463409
|
entity |
| Predicate | distinctFrom |
P1612
|
FINISHED |
| Object | Flora |
—
|
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: Flora | Statement: [Pomona, distinctFrom, Flora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flora Context triple: [Pomona, distinctFrom, Flora]
-
A.
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.
-
B.
Flora
chosen
Flora is a feminine given name of Latin origin meaning "flower," historically associated with the Roman goddess of flowers and spring.
-
C.
Flora
Flora is a popular brand of margarine and other spreadable food products owned by Upfield and marketed for heart health and everyday cooking.
-
D.
Flora
Flora is one of the three good fairies who serve as royal advisors and magical guardians in the animated children's series "Sofia the First."
-
E.
Flora
Flora is a rural municipality in the province of Apayao in the Cordillera Administrative Region of the Philippines.
- 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d3ec857081908da0f974604f2c65 |
completed | April 20, 2026, 7:21 a.m. |
Created at: April 10, 2026, 11:59 a.m.