Triple
T3980453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monastiraki Square |
E85742
|
entity |
| Predicate | commonForeignLanguagesUsed |
P42338
|
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: [Monastiraki Square, commonForeignLanguagesUsed, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonForeignLanguagesUsed Context triple: [Monastiraki Square, commonForeignLanguagesUsed, English]
-
A.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
B.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
C.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
D.
secondLanguageSpeakers
Indicates that the referenced language is spoken as a second (non-native) language by the specified group or number of people.
-
E.
typicalLanguages
chosen
Indicates the languages that are commonly or characteristically used, spoken, or associated with a given entity.
- 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_69aed93908348190a26c8aaf4fab3e86 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa3ef7ac8190abe02f440ff83c43 |
completed | March 9, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69aef8f492ac819089dbb9436dbcdd2b |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:33 p.m.