Triple
T27494771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TZ |
E693993
|
entity |
| Predicate | denotesCountryKnownFor |
P73284
|
FINISHED |
| Object | Mount Kilimanjaro |
—
|
NE NERFINISHED |
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: Mount Kilimanjaro | Statement: [TZ, denotesCountryKnownFor, Mount Kilimanjaro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: denotesCountryKnownFor Context triple: [TZ, denotesCountryKnownFor, Mount Kilimanjaro]
-
A.
countryKnownFor
chosen
Indicates that a country is widely recognized or notable for a particular characteristic, product, activity, or feature.
-
B.
oftenRecognizedBy
Indicates that one entity is frequently identified, acknowledged, or noticed by another entity.
-
C.
notableCountry
Indicates that a country holds particular significance or prominence in relation to the subject entity.
-
D.
countryRepresented
Indicates that one entity serves as the official representative (such as an athlete, diplomat, or delegate) of a specific country in a given context or event.
-
E.
countryOf
Indicates that one entity is the country to which another entity belongs, is located in, or is associated with.
- 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_69ef5382b9648190be0b1ef2ad5d043c |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c663be481908f233d25d28713a4 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 1:07 p.m.