Triple
T4865652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kennywood |
E108762
|
entity |
| Predicate | hasSection |
P35
|
FINISHED |
| Object | Kiddieland |
E176307
|
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: Kiddieland | Statement: [Kennywood, hasSection, Kiddieland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiddieland Context triple: [Kennywood, hasSection, Kiddieland]
-
A.
Kiddyland
chosen
Kiddyland is a children’s amusement area within Playland Park featuring kid-friendly rides and attractions.
-
B.
Kid 'n Play
Kid 'n Play is a late-1980s and early-1990s American hip hop duo best known for their upbeat party rap, signature dance moves, and the House Party film series.
-
C.
Kripp
Kripp is a district of the town of Remagen in the Rhineland-Palatinate region of Germany, situated along the Rhine River.
-
D.
Kiddo
Kiddo is a music producer known for working on the track "Elevation."
-
E.
Toyland
Toyland is the colorful, whimsical fantasy world that serves as the primary setting for Enid Blyton’s Noddy stories, inhabited by living toys and playful characters.
- 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_69bd440b965081908b0557721cae6338 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6d7861e88190ad172632eb5507c7 |
completed | March 20, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be5d0008288190b516fa0bc7458564 |
completed | March 21, 2026, 8:55 a.m. |
Created at: March 20, 2026, 1:26 p.m.