Triple
T9609135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FAN-nee |
E232052
|
entity |
| Predicate | orthographicBasis |
P78269
|
FINISHED |
| Object | spelling of "Fannie" |
E232052
|
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: spelling of "Fannie" | Statement: [FAN-nee, orthographicBasis, spelling of "Fannie"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: spelling of "Fannie" Context triple: [FAN-nee, orthographicBasis, spelling of "Fannie"]
-
A.
FAN-nee
chosen
FAN-nee is the stress pattern indicating that the primary emphasis falls on the first syllable of the name “Fannie.”
-
B.
Fanny Goodwill
Fanny Goodwill is a virtuous and beautiful young woman in Henry Fielding’s novel "Joseph Andrews," serving as the protagonist’s beloved and a model of moral integrity.
-
C.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
-
D.
Finding Fanny
Finding Fanny is a 2014 Indian satirical road comedy film set in Goa that follows a quirky group of characters on a journey to find a postman's long-lost love.
-
E.
Fanning
Fanning is the surname of American actress Dakota Fanning, known for her prominent roles in film and television since childhood.
- 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_69ca8485a90c819094fe40b42fde9d70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a8469e081909c8fb7c84ffea2b3 |
completed | April 1, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d179491ecc8190a72be68cc5f572b2 |
completed | April 4, 2026, 8:49 p.m. |
Created at: March 30, 2026, 8:08 p.m.