Triple
T34064132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Tergat |
E873572
|
entity |
| Predicate | specializedInDistance |
P185428
|
FINISHED |
| Object | 10000 metres |
—
|
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: 10000 metres | Statement: [Paul Tergat, specializedInDistance, 10000 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: specializedInDistance Context triple: [Paul Tergat, specializedInDistance, 10000 metres]
-
A.
raceDistanceSpecialization
chosen
Indicates the specific race distance or range of distances that an entity is particularly specialized or focused on.
-
B.
specializedDistance
Indicates a relationship where the distance between entities is measured or defined using a specific, non-standard metric or specialized criterion.
-
C.
raceDistanceType
Indicates the specific type or category of distance over which a race is conducted.
-
D.
distanceSpecialism
Indicates a relationship where an entity’s area of specialization is specifically in distance-related aspects (such as distance measurement, analysis, or optimization) within a broader domain.
-
E.
distanceSpecialty
Indicates a relationship where an entity’s specialty or expertise is specifically in the field or domain of distance (e.g., distance learning, distance measurement, or distance-related services).
- 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_69f349a4af208190afa14888f9c9fb9d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fb6fdc7eb081908ab8475efb38c430 |
completed | May 6, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69fb5a986e588190b7a10892bd2ff44c |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 1, 2026, 1:52 a.m.