Triple
T2984725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Icefield |
E80593
|
entity |
| Predicate | hasTemporalTrend |
P5318
|
FINISHED |
| Object | shrinking area over recent decades |
—
|
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: shrinking area over recent decades | Statement: [Southern Icefield, hasTemporalTrend, shrinking area over recent decades]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTemporalTrend Context triple: [Southern Icefield, hasTemporalTrend, shrinking area over recent decades]
-
A.
hasTrend
chosen
Indicates that something exhibits or is associated with a particular pattern of change or direction over time.
-
B.
hasTendency
Indicates that an entity is inclined or likely to exhibit a particular behavior, characteristic, or outcome under certain conditions.
-
C.
temporalAspect
Indicates the time-related characteristics or phase (such as duration, frequency, or temporal status) associated with an event or relationship.
-
D.
hasTemporalLocation
Indicates that something occurs, exists, or is valid during a specific time or time interval.
-
E.
hasTense
Indicates that an action, event, or state is associated with a specific grammatical tense (such as past, present, or future).
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99c65ad0819087bb4ae92ab0dc55 |
completed | March 8, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ad9611fc348190a5d17d237f653f60 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:59 p.m.