Triple
T28551173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marlies Schild |
E722887
|
entity |
| Predicate | worldCupOverallBestResult |
P181743
|
FINISHED |
| Object | 2nd place |
—
|
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: 2nd place | Statement: [Marlies Schild, worldCupOverallBestResult, 2nd place]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worldCupOverallBestResult Context triple: [Marlies Schild, worldCupOverallBestResult, 2nd place]
-
A.
worldCupRecord
Indicates a relationship that specifies an entity’s performance statistics or achievements in FIFA World Cup competitions.
-
B.
bestWorldCupPerformance
Indicates the highest level of achievement or furthest stage reached by an entity in any FIFA World Cup tournament.
-
C.
worldCupWon
Indicates that the subject has won the FIFA World Cup tournament.
-
D.
bestWorldCupFinish
Indicates the highest stage or ranking a team or participant has ever achieved in any edition of the World Cup.
-
E.
WorldCupVictories
Indicates the number of times an entity has won 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_69f01a60204481909af1bb76247b8221 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f7824dc3f0819092a5102895b4a478 |
completed | May 3, 2026, 5:13 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
| PDg | Predicate description generation | batch_69f7817c79e081908e685c48165e086b |
completed | May 3, 2026, 5:10 p.m. |
Created at: April 28, 2026, 3:42 a.m.