Triple
T12593283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Mutual Loss and Gain |
E300661
|
entity |
| Predicate | hasOpeningPhrase |
P17856
|
FINISHED |
| Object | Bismillah ir-Rahman ir-Rahim |
—
|
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: Bismillah ir-Rahman ir-Rahim | Statement: [The Mutual Loss and Gain, hasOpeningPhrase, Bismillah ir-Rahman ir-Rahim]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOpeningPhrase Context triple: [The Mutual Loss and Gain, hasOpeningPhrase, Bismillah ir-Rahman ir-Rahim]
-
A.
hasOpening
Indicates that one entity possesses or features an opening, gap, or entrance that allows access, passage, or exposure.
-
B.
hasOpeningSetting
Indicates that one entity (typically a narrative work) has its initial scene or setting located in the other entity.
-
C.
hasOpeningMovement
Indicates that a work, performance, or sequence includes a distinct initial movement or section that begins the overall piece.
-
D.
hasOpeningType
chosen
Indicates that one entity has, features, or is characterized by a particular type or kind of opening.
-
E.
hasOpeningCondition
Indicates that a relationship or action is subject to a specific initial condition that must be met before it can begin or take effect.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954e6e20481908bca684c4b497c48 |
completed | April 10, 2026, 7:52 p.m. |
| PD | Predicate disambiguation | batch_69d95416cbd88190b2c65196162349bc |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 9, 2026, 5:07 p.m.