Triple
T2755812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ghana national football team |
E61096
|
entity |
| Predicate | worldCupAppearanceYear |
P41689
|
FINISHED |
| Object | 2006 |
—
|
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: 2006 | Statement: [Ghana national football team, worldCupAppearanceYear, 2006]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worldCupAppearanceYear Context triple: [Ghana national football team, worldCupAppearanceYear, 2006]
-
A.
worldCupAppearances
Indicates the number of times an entity has participated in a FIFA World Cup tournament.
-
B.
WorldCupAppearances
Indicates the number of times an entity has participated in FIFA World Cup final tournaments.
-
C.
worldCupHostYear
Indicates the year in which a particular country or location served as the host of the FIFA World Cup tournament.
-
D.
activeYearsInWorldCup
Indicates the span of years during which an entity actively participated in World Cup competitions.
-
E.
FIFAWorldCupParticipation
Indicates that an entity has taken part in at least one edition of the FIFA World Cup tournament.
- 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_69ab4b7a85bc819094a349b84beb1f2c |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb718f4c8190bfc34c6597163ebc |
completed | March 7, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69abd82d005c81908a1ac7a1313c6d88 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd91497ec8190927e91ad33549eda |
completed | March 7, 2026, 7:51 a.m. |
Created at: March 6, 2026, 9:56 p.m.