Triple
T11067512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bercy (Paris Métro) |
E261662
|
entity |
| Predicate | hasAdditionalLanguageOfSignage |
P97618
|
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: [Bercy (Paris Métro), hasAdditionalLanguageOfSignage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAdditionalLanguageOfSignage Context triple: [Bercy (Paris Métro), hasAdditionalLanguageOfSignage, English]
-
A.
languageOfSignage
Indicates the language used on signs or written displays associated with an entity.
-
B.
tertiaryLanguageOfSignage
Indicates that a language is used as the third-most prominent language on signage in a given context or location.
-
C.
officialLanguageOfSignage
Indicates that a particular language is the one officially used on public signs and signage within a given place or context.
-
D.
hasSignificantLanguage
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
-
E.
usesSignLanguage
Indicates that one entity communicates using sign language with or in relation to 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79920428c81908db824ab54e08e8d |
completed | April 9, 2026, 12:18 p.m. |
| PD | Predicate disambiguation | batch_69d74411d9e881908c0eeafa0f38e4b6 |
completed | April 9, 2026, 6:15 a.m. |
| PDg | Predicate description generation | batch_69d750c99f9881908ee2b01b6ce4b3a1 |
completed | April 9, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:26 p.m.