Triple
T1516839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nankai Electric Railway |
E32139
|
entity |
| Predicate | providesInformationLanguage |
P3681
|
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: [Nankai Electric Railway, providesInformationLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: providesInformationLanguage Context triple: [Nankai Electric Railway, providesInformationLanguage, English]
-
A.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
-
B.
presentedInLanguage
chosen
Indicates that something (such as content, information, or a work) is expressed or made available using a particular language.
-
C.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
D.
standardLanguageOf
Indicates that one entity serves as the officially recognized or commonly used standard language for another entity (such as a country, region, or organization).
-
E.
languageOfExpression
Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
- 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_69a885e8caf88190a5fbb6159ce87786 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9396e16408190b5e7b0ac43376d81 |
completed | March 5, 2026, 8:06 a.m. |
| PD | Predicate disambiguation | batch_69a907aa67cc81909f00135365447399 |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:26 p.m.