Triple
T15489248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Ngungun |
E377134
|
entity |
| Predicate | hasTypicalReturnTime_hr |
P70126
|
FINISHED |
| Object | 1.5 |
—
|
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: 1.5 | Statement: [Mount Ngungun, hasTypicalReturnTime_hr, 1.5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalReturnTime_hr Context triple: [Mount Ngungun, hasTypicalReturnTime_hr, 1.5]
-
A.
timeOfReturn
Indicates the specific time at which an entity comes back to a prior location or state.
-
B.
hasTypicalUseTime
chosen
Indicates the usual or expected duration or time period during which something is commonly used or in operation.
-
C.
endTimeApproximate
Indicates that the recorded end time of an event or action is not exact but an approximate value.
-
D.
typicalDelivery
Indicates the usual or standard way in which something is delivered, reflecting the most common delivery method or pattern in that context.
-
E.
typicalDurationDays
Indicates the usual or expected number of days that an associated event, process, or state typically lasts.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03faaca588190b0397bc2e27a522a |
completed | April 16, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69ded2874b788190999158e0f043be21 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:48 a.m.