Triple
T551205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goya Award for Best Cinematography |
E11842
|
entity |
| Predicate | timeUnitOfFrequency |
P15625
|
FINISHED |
| Object | year |
—
|
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: year | Statement: [Goya Award for Best Cinematography, timeUnitOfFrequency, year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeUnitOfFrequency Context triple: [Goya Award for Best Cinematography, timeUnitOfFrequency, year]
-
A.
timeEquivalentOf
Indicates that two temporal entities represent the same point in time or duration, possibly expressed in different formats or units.
-
B.
timeType
Indicates the specific temporal category or classification associated with a time-related entity or value (e.g., duration, point in time, interval, or recurrence type).
-
C.
frequency
Indicates how often an event, action, or relationship occurs within a given period or context.
-
D.
timePeriod
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
E.
timeScaleType
Indicates the type or category of temporal scaling applied to an event, process, or measurement (e.g., real-time, accelerated, aggregated).
- 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_69a4932941d08190815efd422f0b4ca7 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a499030cf4819089b9163102255e49 |
completed | March 1, 2026, 7:52 p.m. |
| PD | Predicate disambiguation | batch_69a494bae210819093c2e0d33a8ca51a |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a49858abd48190bd4b002a93e4a908 |
completed | March 1, 2026, 7:49 p.m. |
Created at: March 1, 2026, 7:32 p.m.