Triple
T3696371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mawenzi |
E78468
|
entity |
| Predicate | rankInTanzania |
P45181
|
FINISHED |
| Object | second-highest peak in Tanzania |
—
|
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 peak in Tanzania | Statement: [Mawenzi, rankInTanzania, second-highest peak in Tanzania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInTanzania Context triple: [Mawenzi, rankInTanzania, second-highest peak in Tanzania]
-
A.
rankingInCountry
chosen
Indicates the position or level an entity holds within an ordered list specific to a particular country.
-
B.
rankInSizeInNewZealandIslands
Indicates the ordinal position of an island in New Zealand when ordered by size (e.g., largest, second largest, etc.).
-
C.
NelsonRank
Indicates a ranking or ordered position assigned according to the Nelson ranking system or criteria.
-
D.
hasPopulationRankInTasmania
Indicates the relative position of an entity in the ordered list of population sizes within Tasmania.
-
E.
rankTier
Indicates the classification level or tier assigned to an entity within a ranking or hierarchical system.
- 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_69ad85e3b1888190abc983e06968696d |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc50f9ad88190a926042fa73d65dc |
completed | March 8, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69adb84dc5808190850aa6975cb09e27 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:26 p.m.