Triple
T33965059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montes Caucasus |
E870826
|
entity |
| Predicate | hasReliefCharacterizedAs |
P22470
|
FINISHED |
| Object | steep slopes and sharp ridges |
—
|
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: steep slopes and sharp ridges | Statement: [Montes Caucasus, hasReliefCharacterizedAs, steep slopes and sharp ridges]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReliefCharacterizedAs Context triple: [Montes Caucasus, hasReliefCharacterizedAs, steep slopes and sharp ridges]
-
A.
hasRelief
Indicates that one entity features or exhibits a raised or sculpted surface design (relief) in relation to another entity or context.
-
B.
hasReliefOf
Indicates that one entity features, bears, or is adorned with a sculpted or carved relief representation of another entity.
-
C.
hasReliefFeature
chosen
Indicates that one entity possesses or includes a specific relief-related physical feature or characteristic.
-
D.
hasReliefsFrom
Indicates that one entity experiences a reduction or alleviation of symptoms, problems, or burdens as a result of another entity.
-
E.
hasReliefActor
Indicates that an entity has another entity serving as the actor responsible for providing relief or assistance in a given context.
- 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_69f3499ce8e88190b66e1d49ad8c7037 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f963908190846d232f386fd98f |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:50 a.m.