Triple
T239436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IANA time zone database |
E4894
|
entity |
| Predicate | exampleZoneName |
P9487
|
FINISHED |
| Object | America/New_York |
—
|
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: America/New_York | Statement: [IANA time zone database, exampleZoneName, America/New_York]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleZoneName Context triple: [IANA time zone database, exampleZoneName, America/New_York]
-
A.
zone
Indicates that an entity is located within, associated with, or assigned to a particular geographic or conceptual area or zone.
-
B.
hasZone
Indicates that one entity possesses, contains, or is associated with a specific zone or designated area.
-
C.
regionName
Indicates the name assigned to a specific geographic or administrative region.
-
D.
isCanonicalZone
Indicates that a given zone is the primary, standard, or officially recognized version among possible alternatives.
-
E.
languageZone
Indicates the linguistic region or area in which a language is predominantly used or officially recognized.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25dacf60c8190a5c3ef455b9a8b20 |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b5f27208190ae13f34037fe582b |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25dab745c8190829b7b5e915936e8 |
completed | Feb. 28, 2026, 3:14 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.