Triple
T12482933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cruyff Turn |
E298356
|
entity |
| Predicate | typicalBodyPart |
P105236
|
FINISHED |
| Object | inside of the foot |
—
|
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: inside of the foot | Statement: [Cruyff Turn, typicalBodyPart, inside of the foot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBodyPart Context triple: [Cruyff Turn, typicalBodyPart, inside of the foot]
-
A.
hasBodyRegion
Indicates that an entity possesses, includes, or is associated with a specific anatomical or bodily region.
-
B.
seatOfBody
Indicates that one entity serves as the physical location or base where another entity (typically an organization or authority) is situated or headquartered.
-
C.
injuredBodyPart
Indicates that an entity has sustained an injury specifically affecting a particular body part.
-
D.
bodyContour
Indicates the shape or outline of a body as defined by its external curves and boundaries.
-
E.
typicalBodyType
Indicates that one entity is the usual or characteristic body type associated with another entity.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e8a706c8190873623eab7db607d |
completed | April 10, 2026, 7:24 p.m. |
| PD | Predicate disambiguation | batch_69d94d41f3cc8190a3331fb9a895306f |
completed | April 10, 2026, 7:19 p.m. |
| PDg | Predicate description generation | batch_69d94e87e0e88190bc49dcfe4954c7e0 |
completed | April 10, 2026, 7:24 p.m. |
Created at: April 8, 2026, 9:56 p.m.