Triple
T33898732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Ndereba |
E868986
|
entity |
| Predicate | worldChampionshipsGoldMedalYear |
P20509
|
FINISHED |
| Object | 2003 |
—
|
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: 2003 | Statement: [Catherine Ndereba, worldChampionshipsGoldMedalYear, 2003]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worldChampionshipsGoldMedalYear Context triple: [Catherine Ndereba, worldChampionshipsGoldMedalYear, 2003]
-
A.
WorldChampionshipGoldMedalYear
chosen
Indicates the specific year in which an entity won a gold medal at a world championship event.
-
B.
worldChampionshipMedalYear
Indicates the year in which an entity received a medal at a world championship event.
-
C.
worldChampionshipsGoldMedal
Indicates that the subject has won a gold medal at a world championship competition.
-
D.
worldChampionshipGoldMedals
Indicates the number of gold medals an entity has won at world championship competitions.
-
E.
WorldChampionshipMedalsTotal
Indicates the total number of medals an entity has won at world championship competitions.
- 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_69fdec5ffe088190ac5505f26c6cff18 |
completed | May 8, 2026, 2 p.m. |
| PD | Predicate disambiguation | batch_69fdeae15f1c81908fc63fbc1b028d2e |
completed | May 8, 2026, 1:53 p.m. |
Created at: May 1, 2026, 1:48 a.m.