Triple
T12829757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hand of God goal |
E306751
|
entity |
| Predicate | ballContactBodyPart |
P107107
|
FINISHED |
| Object | left hand |
—
|
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: left hand | Statement: [Hand of God goal, ballContactBodyPart, left hand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ballContactBodyPart Context triple: [Hand of God goal, ballContactBodyPart, left hand]
-
A.
injuredBodyPart
Indicates that an entity has sustained an injury specifically affecting a particular body part.
-
B.
typicalBodyPart
Indicates that one entity is a body part that is characteristically or normally associated with another entity.
-
C.
ballContactCount
Indicates the number of times an entity has made contact with a ball within a given context or timeframe.
-
D.
protectsBodyPart
Indicates that one entity serves to shield, guard, or defend a specific body part from harm or damage.
-
E.
hasBodyRegion
Indicates that an entity possesses, includes, or is associated with a specific anatomical or bodily region.
- 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714208f881908f7f8a921362909a |
completed | April 10, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69d96fa08cd481909a946046ba63809f |
completed | April 10, 2026, 9:46 p.m. |
| PDg | Predicate description generation | batch_69d9713e45a88190acd346f066093550 |
completed | April 10, 2026, 9:53 p.m. |
Created at: April 9, 2026, 5:34 p.m.