Triple
T21770407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apopka High School |
E537409
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Apopka |
—
|
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: Apopka | Statement: [Apopka High School, city, Apopka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apopka Context triple: [Apopka High School, city, Apopka]
-
A.
Apopka, Florida
chosen
Apopka, Florida is a city in central Florida known as the "Indoor Foliage Capital of the World" for its extensive greenhouse nurseries and plant industry.
-
B.
Altamonte Springs
Altamonte Springs is a suburban city in the Orlando metropolitan area of Central Florida, known for its residential communities, shopping centers, and recreational amenities.
-
C.
Altadena
Altadena is an unincorporated community in Los Angeles County, California, located in the foothills of the San Gabriel Mountains just north of Pasadena.
-
D.
Lauderdale Maitland
Lauderdale Maitland was a British actor active in the early 20th century, known for his work on stage and in silent films.
-
E.
Eustis
Eustis is a surname of English origin borne by various notable individuals, including military figures and public officials in American history.
- 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_69e0c46f5d1c8190bf830409e98464e5 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f031ad76848190b2c7a05d091b7faf |
completed | April 28, 2026, 4:03 a.m. |
Created at: April 16, 2026, 6:51 p.m.