Triple
T33898734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Ndereba |
E868986
|
entity |
| Predicate | worldChampionshipsSilverMedalIn |
P15198
|
FINISHED |
| Object | marathon |
—
|
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: marathon | Statement: [Catherine Ndereba, worldChampionshipsSilverMedalIn, marathon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worldChampionshipsSilverMedalIn Context triple: [Catherine Ndereba, worldChampionshipsSilverMedalIn, marathon]
-
A.
worldChampionshipSilverMedals
chosen
Indicates that the subject has won one or more silver medals at a world championship competition.
-
B.
worldChampionshipSilverYear
Indicates the year in which an entity achieved a silver (second-place) result at a world championship.
-
C.
EuropeanChampionshipSilverMedals
Indicates that the subject has won a silver medal in a European Championship competition.
-
D.
olympicSilverMedals
Indicates that the subject has won one or more silver medals at the Olympic Games.
-
E.
wonWorldCupSilverMedal
Indicates that an entity achieved second place and received a silver medal in a World Cup competition.
- 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_69f34997703c8190866b1d404bce531f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fdee770af48190aca2670db50f8b49 |
completed | May 8, 2026, 2:08 p.m. |
| PD | Predicate disambiguation | batch_69fdecec98a08190a357d816dc2a6dbe |
completed | May 8, 2026, 2:02 p.m. |
Created at: May 1, 2026, 1:48 a.m.