Triple
T32470816
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krisztina Egerszegi |
E829842
|
entity |
| Predicate | ageAtFirstOlympicGold |
P174486
|
FINISHED |
| Object | 14 |
—
|
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: 14 | Statement: [Krisztina Egerszegi, ageAtFirstOlympicGold, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageAtFirstOlympicGold Context triple: [Krisztina Egerszegi, ageAtFirstOlympicGold, 14]
-
A.
firstOlympicMedalist
Indicates that the subject is the first entity ever to win an Olympic medal for the object (such as a country, team, or group).
-
B.
olympicDebut
Indicates the event or year in which an entity first participated in the Olympic Games.
-
C.
youthOlympicDebutYear
Indicates the year in which an entity first participated in or was introduced to the Youth Olympic Games.
-
D.
olympicGoldWith
Indicates that the related entities won an Olympic gold medal together, typically as teammates in the same event or competition.
-
E.
wonOlympicGoldYear
Indicates that an entity won an Olympic gold medal in the specified year.
- 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_69f3491ee87c81908cbf5890079c2af6 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c355c35c819091b633137f54c14a |
completed | May 3, 2026, 3:39 a.m. |
| PD | Predicate disambiguation | batch_69f6bd25bed08190befcabd3a41ffadf |
completed | May 3, 2026, 3:12 a.m. |
| PDg | Predicate description generation | batch_69f6c1b666188190ac43c3011a7df048 |
completed | May 3, 2026, 3:32 a.m. |
Created at: May 1, 2026, 12:57 a.m.