Triple
T38331047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lenana |
E1037822
|
entity |
| Predicate | rankingOnMountKenya |
P109627
|
FINISHED |
| Object | third-highest peak |
—
|
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: third-highest peak | Statement: [Lenana, rankingOnMountKenya, third-highest peak]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingOnMountKenya Context triple: [Lenana, rankingOnMountKenya, third-highest peak]
-
A.
rankingOnMountKenyaByHeight
chosen
Indicates the relative position of something in an ordered list of items on Mount Kenya, based on their height.
-
B.
rankByLengthInKenya
Indicates ordering or comparing entities based on their length specifically within the context or boundaries of Kenya.
-
C.
rankingByHeightInAfrica
Indicates that entities are ordered or compared based on their height specifically within the context of Africa.
-
D.
rankByHeightNepal
Indicates an ordering of entities based on their relative heights specifically within the context of Nepal.
-
E.
distanceFromNairobi
Indicates the spatial distance between a given entity’s location and the city of Nairobi.
- 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_69f76e20d65c81909619ac0dd85c56f0 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: May 3, 2026, 4:30 p.m.