Triple
T36901920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helter Skelter |
E912059
|
entity |
| Predicate | riderEquipment |
P186668
|
FINISHED |
| Object | coconut mat |
—
|
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: coconut mat | Statement: [Helter Skelter, riderEquipment, coconut mat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: riderEquipment Context triple: [Helter Skelter, riderEquipment, coconut mat]
-
A.
riderType
Indicates the category or role of a rider in relation to a ride, transport service, or vehicle (e.g., passenger, driver, courier).
-
B.
horseEquipment
Indicates that one entity is equipment or gear specifically used for or associated with a horse.
-
C.
crewEquipment
Indicates that certain equipment is assigned to, used by, or associated with a specific crew.
-
D.
equippedFor
Indicates that one entity is suitably provided with the necessary tools, features, or capabilities to perform a particular function or handle a specific situation for another entity or purpose.
-
E.
hasArrestingGear
Indicates that an entity is equipped with a system or mechanism used to rapidly decelerate and stop another entity, typically during landing or capture.
- 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_69f76e841b54819097e7fa768bbc70b2 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fe1a1ca4819084c196f0041f0be2 |
completed | May 5, 2026, 2:26 p.m. |
| PD | Predicate disambiguation | batch_69f7cf79ddb08190a083405cccc14137 |
completed | May 3, 2026, 10:43 p.m. |
| PDg | Predicate description generation | batch_69f9fd66eed48190bdc26a8def328c2d |
completed | May 5, 2026, 2:23 p.m. |
Created at: May 3, 2026, 4:13 p.m.