Triple
T267958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China Standard Time |
E5773
|
entity |
| Predicate | numberOfOfficialTimeZonesInCountry |
P4883
|
FINISHED |
| Object | 1 |
—
|
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 | Statement: [China Standard Time, numberOfOfficialTimeZonesInCountry, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOfficialTimeZonesInCountry Context triple: [China Standard Time, numberOfOfficialTimeZonesInCountry, 1]
-
A.
hasTimeZones
Indicates that an entity is associated with one or more time zones in which it is valid or operates.
-
B.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
C.
capitalTimeZoneOf
Indicates that a specified time zone is the primary or official time zone in which a given capital city is located or operates.
-
D.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
-
E.
isSingleTimeZoneForCountry
chosen
Indicates that a country uses a single, uniform time zone throughout its entire territory.
- 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_69a2587daeb081909591b9d30f80a271 |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25dae4a0c8190a66cf6ed3889851c |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b70d99c819085d8381a313a2a34 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:56 a.m.