Triple
T23889523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | بنسيون ميرامار |
E600721
|
entity |
| Predicate | يملكه_في_الرواية |
P153951
|
FINISHED |
| Object | الست ماريانا |
—
|
NE NERFINISHED |
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: الست ماريانا | Statement: [بنسيون ميرامار, يملكه_في_الرواية, الست ماريانا]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: يملكه_في_الرواية Context triple: [بنسيون ميرامار, يملكه_في_الرواية, الست ماريانا]
-
A.
guardedByInFiction
Indicates that one fictional entity is protected or watched over by another within a narrative context.
-
B.
literaryWorkInStory
Indicates that one literary work is referenced, featured, or embedded within the narrative of another story.
-
C.
containsBook
Indicates that one entity (typically a container or collection) includes a specific book as part of its contents.
-
D.
عدد الحروف
Indicates the relationship that specifies the number of letters contained in a given word or text.
-
E.
book1Contains
Indicates that one book includes, encloses, or has as part of its content another specified element or section.
- 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_69e295341ac0819080647f2908af793c |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1cd029be48190b9319e59bf5d3a4d |
completed | April 29, 2026, 9:18 a.m. |
| PD | Predicate disambiguation | batch_69f1614e24b48190a1c8fb5b7c75ee0f |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 8:25 p.m.