Triple
T9299080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tarzan and the Mermaids |
E223713
|
entity |
| Predicate | featuresStunts |
P45832
|
FINISHED |
| Object | diving sequences |
—
|
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: diving sequences | Statement: [Tarzan and the Mermaids, featuresStunts, diving sequences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresStunts Context triple: [Tarzan and the Mermaids, featuresStunts, diving sequences]
-
A.
hasStunts
chosen
Indicates that one entity performs, includes, or is associated with stunt actions for another entity or context.
-
B.
featuresStar
Indicates that one entity prominently includes or showcases another entity as a main star or featured performer.
-
C.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
D.
featuresMechanic
Indicates that something includes or incorporates a particular mechanic as part of its design or functionality.
-
E.
featuresVehicle
Indicates that one entity includes, presents, or prominently incorporates a particular vehicle as part of its content, composition, or offering.
- F. None of above.
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_69ca8423edb08190bc0c91287a484768 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd08cf50cc8190a025f478dff4f9fd |
completed | April 1, 2026, noon |
| PD | Predicate disambiguation | batch_69cc7a5ef1908190bc5ca166bb895af6 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:36 p.m.