Triple
T12482950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cruyff Turn |
E298356
|
entity |
| Predicate | effectOnDefender |
P100772
|
FINISHED |
| Object | wrong‑footing the defender |
—
|
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: wrong‑footing the defender | Statement: [Cruyff Turn, effectOnDefender, wrong‑footing the defender]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnDefender Context triple: [Cruyff Turn, effectOnDefender, wrong‑footing the defender]
-
A.
attackEffect
chosen
Indicates that one entity’s attack produces a specific effect or consequence on another entity.
-
B.
effectivenessAgainst
Indicates how well one entity performs in countering, influencing, or mitigating the impact of another entity.
-
C.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
-
D.
hasBaseDefense
Indicates that an entity possesses a specified level or value of defensive capability in its default or starting state.
-
E.
hasDefenderStrength
Indicates that an entity possesses a certain level or measure of defensive capability or protective power.
- 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_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. |
Created at: April 8, 2026, 9:56 p.m.