Triple
T24974730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Disney Research |
E624985
|
entity |
| Predicate | appliesTechnologyIn |
P160830
|
FINISHED |
| Object | theme parks |
—
|
LITERAL 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: theme parks | Statement: [Disney Research, appliesTechnologyIn, theme parks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesTechnologyIn Context triple: [Disney Research, appliesTechnologyIn, theme parks]
-
A.
laterUsedTechnology
Indicates that one entity adopted or employed a technology after another entity had already used it.
-
B.
enablesTechnology
Indicates that one entity makes it possible for another entity, system, or process to function through the use or provision of a particular technology.
-
C.
technologyPioneered
Indicates that an entity was the first or among the first to develop, introduce, or significantly advance a particular technology.
-
D.
introducedTechnologyTo
Indicates that one entity was responsible for presenting, bringing, or implementing a particular technology to another entity or context for the first time.
-
E.
usesTechnologyInStory
Indicates that an entity incorporates or employs a particular technology within the context of a narrative or story.
- F. None of above. chosen
Provenance (4 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_69e2ff24512481908e9a72315b8d0354 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f60ac643108190ae81561267155791 |
completed | May 2, 2026, 2:31 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69f606c15af88190958856a9e467b826 |
completed | May 2, 2026, 2:14 p.m. |
Created at: April 18, 2026, 6:01 a.m.