Triple
T33898756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Ndereba |
E868986
|
entity |
| Predicate | hasWonMajorMarathons |
P128376
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Catherine Ndereba, hasWonMajorMarathons, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonMajorMarathons Context triple: [Catherine Ndereba, hasWonMajorMarathons, yes]
-
A.
marathonAchievement
chosen
Indicates that an entity has successfully completed or achieved a significant result in a marathon event.
-
B.
numberOfNYCMarathonWins
Indicates the number of times an entity has won the New York City Marathon.
-
C.
hasHostedMajorChampionship
Indicates that an entity has served as the venue or organizer for at least one significant, high-level championship event in its domain.
-
D.
bostonMarathonWins
Indicates the relationship where an entity has achieved one or more victories in the Boston Marathon.
-
E.
marathonWinner
Indicates that one entity is the competitor who finished first and won a specified marathon event.
- 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_69fdf5d05cc481909ec9e1b1f0784279 |
completed | May 8, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69fdf0cdd6948190838864ab3120dfa6 |
completed | May 8, 2026, 2:18 p.m. |
Created at: May 1, 2026, 1:48 a.m.