Triple
T3145156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | K-123 hill |
E65744
|
entity |
| Predicate | KPoint |
P46344
|
FINISHED |
| Object | 123 m |
—
|
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: 123 m | Statement: [K-123 hill, KPoint, 123 m]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: KPoint Context triple: [K-123 hill, KPoint, 123 m]
-
A.
hasKPoint
Indicates that an entity possesses or is associated with a specific K-point, typically a designated point in reciprocal or parameter space.
-
B.
pointDefinition
Indicates that one entity serves as the defining description or specification of a particular point in another entity.
-
C.
points
Indicates that one entity directs attention, focus, or a physical/abstract indication toward another entity or location.
-
D.
isReferencePointFor
Indicates that one entity serves as a positional or conceptual basis used to locate, measure, or interpret another entity.
-
E.
interactionPoint
Indicates a specific location or moment where two or more entities come into contact or engage with each other.
- 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_69ad8582f564819088c27e1f96153938 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada59797788190a8d71262888c5df0 |
completed | March 8, 2026, 4:36 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfa3d9081908425aa636bb9b897 |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada148e9108190b363dd0f1a94ac8e |
completed | March 8, 2026, 4:18 p.m. |
Created at: March 8, 2026, 3:05 p.m.