Triple
T25814369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | الروضة الشريفة |
E650207
|
entity |
| Predicate | hasEntrySystem |
P155183
|
FINISHED |
| Object | حجز مسبق عبر التطبيقات الرسمية في السعودية |
—
|
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: حجز مسبق عبر التطبيقات الرسمية في السعودية | Statement: [الروضة الشريفة, hasEntrySystem, حجز مسبق عبر التطبيقات الرسمية في السعودية]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEntrySystem Context triple: [الروضة الشريفة, hasEntrySystem, حجز مسبق عبر التطبيقات الرسمية في السعودية]
-
A.
usesEntrySystem
chosen
Indicates that an entity operates or interacts through a particular entry or access control system.
-
B.
hasEntryOn
Indicates that one entity contains or includes an entry, record, or listing about another entity.
-
C.
hasEntryType
Indicates that something is associated with a specific category or type of entry within a system or dataset.
-
D.
hasEntryExample
Indicates that something includes or is associated with a specific example illustrating one of its entries.
-
E.
hasRegisterSystem
Indicates that an entity uses or is associated with a particular register system (e.g., a system for recording, tracking, or registering items, events, or participants).
- 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_69e7ab35d264819095367f7e80c983ff |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f657f653448190a945b4751af8507d |
completed | May 2, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69f6575ba12081909396036f78757a76 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 22, 2026, 7:12 a.m.