Triple
T8990314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | خادم الحرمين الشريفين |
E214770
|
entity |
| Predicate | يُذكر_مع |
P63729
|
FINISHED |
| Object | ملك المملكة العربية 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: ملك المملكة العربية السعودية | Statement: [خادم الحرمين الشريفين, يُذكر_مع, ملك المملكة العربية السعودية]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: يُذكر_مع Context triple: [خادم الحرمين الشريفين, يُذكر_مع, ملك المملكة العربية السعودية]
-
A.
recalledBy
Indicates that an entity is brought back, retrieved, or remembered as a result of another entity’s action or prompting.
-
B.
memorizedBy
Indicates that some content, information, or material has been learned and retained in memory by a particular entity.
-
C.
rememberedThrough
Indicates that one entity is remembered, commemorated, or kept in memory by means of, or through the influence of, another entity or medium.
-
D.
mentionedWith
chosen
Indicates that two entities are mentioned together or in close association within the same context, such as a document, sentence, or conversation.
-
E.
rememberedFor
Indicates that one entity is known or recognized primarily because of, or in association with, another entity or achievement.
- 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_69ca839f76bc8190a4b7123cdd682199 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc68733548819096a5ba0ff41e43da |
completed | April 1, 2026, 12:36 a.m. |
| PD | Predicate disambiguation | batch_69cc5edba0f88190b97401636a076d7a |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:04 p.m.