Triple
T19461409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BurJuman Metro Station |
E486879
|
entity |
| Predicate | ticketOfficeLanguageSupport |
P112988
|
FINISHED |
| Object | Arabic |
—
|
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: Arabic | Statement: [BurJuman Metro Station, ticketOfficeLanguageSupport, Arabic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketOfficeLanguageSupport Context triple: [BurJuman Metro Station, ticketOfficeLanguageSupport, Arabic]
-
A.
hasCustomerServiceLanguage
chosen
Indicates that an entity provides customer service in a specified language or set of languages.
-
B.
serviceBranchLanguage
Indicates the language or languages used or officially recognized by a particular branch of a service (such as a military or organizational branch).
-
C.
hasLanguageRestriction
Indicates that an entity is subject to limitations or specific conditions regarding the languages it can use, support, or be associated with.
-
D.
eligibleLanguage
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
-
E.
languageOfVenue
Indicates the language primarily used or officially designated for communication at a given venue.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c983f481908b2684dc4380b889 |
completed | April 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7499a4819082bec0be8afba35c |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.