Triple
T9241621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manila–Tokyo |
E222071
|
entity |
| Predicate | languageUsedOnRoute |
P87746
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Manila–Tokyo, languageUsedOnRoute, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageUsedOnRoute Context triple: [Manila–Tokyo, languageUsedOnRoute, English]
-
A.
languageOfRouteCountries
Indicates the languages used or officially spoken in the countries through which a given route passes.
-
B.
navigationLanguage
Indicates the language used for navigation-related content, such as menus, directions, or interface controls.
-
C.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
-
D.
languageOfOperation
Indicates the language in which an entity (such as a system, service, or process) primarily operates or functions.
-
E.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
- 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_69ca83ee26cc81909ac624e190597d6d |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cd03ea9d90819096f9ca5321dffd56 |
completed | April 1, 2026, 11:39 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4765648190aa9445c4a22dc471 |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:30 p.m.