Triple
T4103203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanga Parbat |
E88387
|
entity |
| Predicate | countryHighestPeaksRank |
P53391
|
FINISHED |
| Object | second highest mountain in Pakistan |
—
|
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 mountain in Pakistan | Statement: [Nanga Parbat, countryHighestPeaksRank, second highest mountain in Pakistan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryHighestPeaksRank Context triple: [Nanga Parbat, countryHighestPeaksRank, second highest mountain in Pakistan]
-
A.
countryHighestPointRank
Indicates the relative ranking of a country's highest natural elevation compared to the highest points of other countries.
-
B.
notablePeak
Indicates that one entity is a peak or summit that is especially prominent, famous, or significant in relation to another entity.
-
C.
summitElevationRank
Indicates the relative position of a summit in an ordered list based on its elevation compared to other summits.
-
D.
rankByHeightWorld
Indicates an ordering of entities based on their relative height compared to all others in the world.
-
E.
territorialPeak
Indicates the highest geographical point located within the territory or jurisdiction of a given entity.
- 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_69aed9484fb881909146f4c772ad277c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefd116dac8190952cb2ddf63216ec |
completed | March 9, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69aef90b2ef08190ae84febfd69dd48b |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefa5c52648190b001027f4dba75cb |
completed | March 9, 2026, 4:50 p.m. |
Created at: March 9, 2026, 3:40 p.m.