Triple
T19970894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fun Zone |
E480069
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Fun Zone |
—
|
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: Fun Zone | Statement: [Fun Zone, name, Fun Zone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fun Zone Context triple: [Fun Zone, name, Fun Zone]
-
A.
Fun Zone
chosen
Fun Zone was a popular amusement area featuring rides and entertainment that served as a key draw for visitors to the California Pacific International Exposition in San Diego in the mid-1930s.
-
B.
Fun World
Fun World is a costume and novelty company best known for creating the iconic Ghostface mask from the Scream horror film franchise.
-
C.
Fort Fun
Fort Fun is a playful nickname for Fort Collins, Colorado, highlighting the city’s lively, recreation-focused atmosphere.
-
D.
Fun and Games
"Fun and Games" is the title of the first act of Edward Albee’s play "Who’s Afraid of Virginia Woolf?", in which a seemingly lighthearted evening gradually reveals the toxic dynamics of a middle-aged couple’s marriage.
-
E.
Playground Entertainment
Playground Entertainment is a British-American television and film production company known for creating high-quality scripted dramas and literary adaptations.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc89b508190879d29bef546aac8 |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.