Triple
T4996359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2007 College World Series |
E112255
|
entity |
| Predicate | runnerUpCWSAppearancesCount |
P61384
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [2007 College World Series, runnerUpCWSAppearancesCount, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runnerUpCWSAppearancesCount Context triple: [2007 College World Series, runnerUpCWSAppearancesCount, 8]
-
A.
WorldChampionshipAppearances
Indicates the number of times an entity has participated in a world championship competition.
-
B.
runnerUpConsecutiveAppearances
Indicates that an entity has achieved runner-up status in a competition for a specified number of consecutive appearances or editions.
-
C.
worldCupAppearances
Indicates the number of times an entity has participated in a FIFA World Cup tournament.
-
D.
associatedChampionshipCount
Indicates the number of championships that are linked or related to a given entity.
-
E.
afconRunnersUpCount
Indicates the number of times an entity has finished as runners-up in the Africa Cup of Nations (AFCON) 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_69bd4432b32c81909f3b3c6bd10f0653 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7472a1dc8190942f568a81fdd961 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd714aee2481908fb0dd5fa2daf3a1 |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd74713fc88190916c2b04cd2e677e |
completed | March 20, 2026, 4:23 p.m. |
Created at: March 20, 2026, 1:34 p.m.