Triple
T30915692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan–Philippines air travel market |
E787571
|
entity |
| Predicate | seasonalityPattern |
P97591
|
FINISHED |
| Object | peakDuringGoldenWeek |
—
|
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: peakDuringGoldenWeek | Statement: [Japan–Philippines air travel market, seasonalityPattern, peakDuringGoldenWeek]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seasonalityPattern Context triple: [Japan–Philippines air travel market, seasonalityPattern, peakDuringGoldenWeek]
-
A.
hasSeasonalPattern
Indicates that the occurrence, intensity, or characteristics of something regularly vary according to a recurring seasonal cycle.
-
B.
primarySeasonality
chosen
Indicates the main or most characteristic time period or season during which something typically occurs or is most relevant.
-
C.
hasSeasonalStructure
Indicates that something exhibits a recurring pattern, organization, or behavior that varies systematically with the seasons.
-
D.
seasonalDirection
Indicates the direction or trend of change in a phenomenon as it varies across different seasons.
-
E.
hasSeasonalNature
Indicates that something exhibits characteristics, behavior, or occurrence patterns that vary according to specific seasons or times of the year.
- 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_69f224be300c8190a6513ce1ee0a7026 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: April 29, 2026, 8:51 p.m.