Triple
T20254579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daisy Kadibil |
E498650
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Daisy |
—
|
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: Daisy | Statement: [Daisy Kadibil, givenName, Daisy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daisy Context triple: [Daisy Kadibil, givenName, Daisy]
-
A.
Daisy
chosen
Daisy is a feminine given name commonly associated with the daisy flower and often used in English-speaking countries.
-
B.
Daisy
Daisy is a fictional rhinoceros character, typically depicted as a gentle, anthropomorphic animal in children's stories or media.
-
C.
Daisy
Daisy is a central character in Margaret Atwood's dystopian novel "The Testaments," whose perspective helps reveal the inner workings and resistance within the totalitarian regime of Gilead.
-
D.
Daisy
Daisy is the central protagonist of "The Mystery Series," around whom the stories' investigations and adventures revolve.
-
E.
Daisy
Daisy is a central female character in the 2003 independent film "The Brown Bunny," serving as the emotional focus of the protagonist's memories and guilt.
- 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e673aa42348190852ae8313f4494ca |
completed | April 20, 2026, 6:42 p.m. |
Created at: April 11, 2026, 11:41 p.m.