Triple
T33546219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morocco national under-17 football team |
E859213
|
entity |
| Predicate | bestFifaU17WorldCupResultYear |
P165444
|
FINISHED |
| Object | 2013 |
—
|
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: 2013 | Statement: [Morocco national under-17 football team, bestFifaU17WorldCupResultYear, 2013]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestFifaU17WorldCupResultYear Context triple: [Morocco national under-17 football team, bestFifaU17WorldCupResultYear, 2013]
-
A.
bestResultFifaU17WWCYear
Indicates the year in which an entity achieved its best performance or result at the FIFA U-17 Women's World Cup.
-
B.
bestResultFIFA U-20 World CupYear
Indicates the best performance or final standing an entity achieved in a given year’s FIFA U-20 World Cup.
-
C.
bestResultFifaU17WWC
Indicates that the object is the best performance or highest achievement an entity has attained in the FIFA U-17 Women's World Cup.
-
D.
bestResultFifaU17WorldCup
chosen
Indicates the highest placement or achievement an entity has attained in the FIFA U-17 World Cup tournament.
-
E.
fifaU17WorldCupWinningYear
Indicates the specific year in which a given team won the FIFA U-17 World Cup.
- 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_69f3497a5be08190a39b12736899e034 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f70c17d88190aa74afc2dd2a0467 |
completed | May 3, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:39 a.m.