Triple
T24685004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Europe/Paris |
E611256
|
entity |
| Predicate | javaTimeZoneId |
P101835
|
FINISHED |
| Object | Europe/Paris |
—
|
NE NERFINISHED |
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: Europe/Paris | Statement: [Europe/Paris, javaTimeZoneId, Europe/Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: javaTimeZoneId Context triple: [Europe/Paris, javaTimeZoneId, Europe/Paris]
-
A.
javaTimeZoneID
chosen
Indicates that an entity is associated with a specific Java time zone identifier (e.g., "America/New_York") used for date and time operations.
-
B.
timeZoneType
Indicates the classification or category of a time zone associated with an entity (e.g., standard, daylight, or specific time zone format/type).
-
C.
MicrosoftTimeZoneIdentifier
Indicates the specific Microsoft-defined time zone identifier associated with an entity or event.
-
D.
timeZoneName
Indicates the specific time zone designation (such as its standard name or label) associated with an entity.
-
E.
timeZoneEndpoint
Indicates a connection or boundary point where a specific time zone applies or is defined.
- 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_69e2c4d678b081908910f4271627a31a |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f410fe3b848190ae296a29f742ee30 |
completed | May 1, 2026, 2:33 a.m. |
| PD | Predicate disambiguation | batch_69f40ee8ada8819089a7016b50308ff0 |
completed | May 1, 2026, 2:24 a.m. |
Created at: April 18, 2026, 3:17 a.m.