Triple
T35089703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Siple |
E1012682
|
entity |
| Predicate | hasNoPermanentHumanSettlement |
P45262
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Mount Siple, hasNoPermanentHumanSettlement, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoPermanentHumanSettlement Context triple: [Mount Siple, hasNoPermanentHumanSettlement, true]
-
A.
hasNoUrbanSettlement
Indicates that the referenced area or region does not contain any urban settlements such as towns or cities.
-
B.
hasNoPermanentSettlements
chosen
Indicates that the subject does not maintain any long-term or stable inhabited locations.
-
C.
hasNotableSettlementRegion
Indicates that an entity has a significant or well-known settlement located within a specified geographic region.
-
D.
hasNoCoastline
Indicates that the referenced place is landlocked and does not border any sea or ocean.
-
E.
hadNoSignificantUrbanSettlementBy
Indicates that a given place did not possess any major or notable urban settlement up to and including the specified time.
- 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_69f76dd432ec8190969bc32acfc152b1 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a036054129081909e66c5d62015e6c1 |
completed | May 12, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_6a035f17cbf88190acd6bdbfc9789ffb |
completed | May 12, 2026, 5:10 p.m. |
Created at: May 3, 2026, 4:01 p.m.