Triple
T24300465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Damson Idris |
E606079
|
entity |
| Predicate | seriesFormatOfSnowfall |
P155469
|
FINISHED |
| Object | serialized narrative |
—
|
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: serialized narrative | Statement: [Damson Idris, seriesFormatOfSnowfall, serialized narrative]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seriesFormatOfSnowfall Context triple: [Damson Idris, seriesFormatOfSnowfall, serialized narrative]
-
A.
hasSnowfallFrequency
Indicates how often snowfall occurs for or at a given entity.
-
B.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
-
C.
hasSnowfallUnit
Indicates the unit of measurement used to express the amount or depth of snowfall in a given context.
-
D.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
E.
averageAnnualSnowfall
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
- 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_69e29549335881909cbf27adcaba1cf0 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f2915e4ffc8190bf711dae443b3ec1 |
completed | April 29, 2026, 11:16 p.m. |
| PD | Predicate disambiguation | batch_69f1c45c6ec081908401b69424428100 |
completed | April 29, 2026, 8:42 a.m. |
| PDg | Predicate description generation | batch_69f1c6d4e99081909f61899eccafb73e |
completed | April 29, 2026, 8:52 a.m. |
Created at: April 18, 2026, 12:09 a.m.