Triple
T18377592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1896 Olympics men’s 100 metre freestyle |
E446356
|
entity |
| Predicate | silverMedalistNOC |
P83402
|
FINISHED |
| Object | AUT |
—
|
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: AUT | Statement: [1896 Olympics men’s 100 metre freestyle, silverMedalistNOC, AUT]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: silverMedalistNOC Context triple: [1896 Olympics men’s 100 metre freestyle, silverMedalistNOC, AUT]
-
A.
silverMedalistCountry
chosen
Indicates the country that achieved second place (won the silver medal) in a given competition or event.
-
B.
bronzeMedalistNation
Indicates the nation that received the bronze medal in a given event or competition.
-
C.
silverMedalist
Indicates that an entity finished in second place in a competition or event, earning the silver medal.
-
D.
goldMedalistNationality
Indicates the nationality of the athlete who won the gold medal in a given event or competition.
-
E.
olympicSilverMedals
Indicates that the subject has won one or more silver medals at the Olympic Games.
- 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e5179919c881908d55ea24f93c5827 |
completed | April 19, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69e44ff1f92c8190afbb8e85d12bf2a9 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:45 a.m.