Triple
T28880075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mlima Meru |
E732390
|
entity |
| Predicate | relativeHeightInTanzania |
P202521
|
FINISHED |
| Object | second highest after Mount Kilimanjaro |
—
|
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: second highest after Mount Kilimanjaro | Statement: [Mlima Meru, relativeHeightInTanzania, second highest after Mount Kilimanjaro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeHeightInTanzania Context triple: [Mlima Meru, relativeHeightInTanzania, second highest after Mount Kilimanjaro]
-
A.
regionWithinTanzania
Indicates that one region is geographically located within the national boundaries of Tanzania.
-
B.
statusInTanzania
Indicates the legal, social, or operational standing or condition that an entity has within the context of Tanzania.
-
C.
distanceToMountKilimanjaro
Indicates the measured or estimated spatial distance between a given entity and Mount Kilimanjaro.
-
D.
rankingOnMountKenyaByHeight
Indicates the relative position of something in an ordered list of items on Mount Kenya, based on their height.
-
E.
distanceToArusha
Indicates the measured spatial distance between a given entity and the location Arusha.
- F. None of above. chosen
Provenance (4 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_69f05b06807c81909b4bbd4c20403a2b |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_6a008d6085508190a71c52cd028a6297 |
completed | May 10, 2026, 1:51 p.m. |
| PD | Predicate disambiguation | batch_6a008ced31448190b8fc60bf87b40647 |
completed | May 10, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_6a008d5fe63c8190ab4f5a31c249c674 |
completed | May 10, 2026, 1:51 p.m. |
Created at: April 28, 2026, 7:42 a.m.