Triple
T21630503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siphiwe Tshabalala scored a long-range opening goal |
E533815
|
entity |
| Predicate | hasFootUsed |
P145306
|
FINISHED |
| Object | left 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: left foot | Statement: [Siphiwe Tshabalala scored a long-range opening goal, hasFootUsed, left foot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFootUsed Context triple: [Siphiwe Tshabalala scored a long-range opening goal, hasFootUsed, left foot]
-
A.
hasFootName
Indicates that an entity has a foot (or feet) identified by a specific name.
-
B.
hasFootCrossing
Indicates that one entity has a designated crossing point specifically intended for pedestrians on foot.
-
C.
colorOfFoot
Indicates the specific color attribute associated with a foot.
-
D.
footType
Indicates the specific kind or classification of feet that an entity possesses or is characterized by.
-
E.
hasFootstomps
Indicates that one entity performs or possesses foot-stomping actions or behaviors in relation to another entity or context.
- 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_69e0c464fba881908d0ff2ac80511ce1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef521679fc81909c94d42439fda4ba |
completed | April 27, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69e69677b9c48190bf81f795aa8ad74e |
completed | April 20, 2026, 9:11 p.m. |
| PDg | Predicate description generation | batch_69e69cb4bcbc8190a4fc2d508df107be |
completed | April 20, 2026, 9:37 p.m. |
Created at: April 16, 2026, 6:34 p.m.