Triple
T15298360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Disbelievers |
E365719
|
entity |
| Predicate | containsPhraseEnglish |
P24842
|
FINISHED |
| Object | To you your religion, and to me my religion. |
—
|
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: To you your religion, and to me my religion. | Statement: [The Disbelievers, containsPhraseEnglish, To you your religion, and to me my religion.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsPhraseEnglish Context triple: [The Disbelievers, containsPhraseEnglish, To you your religion, and to me my religion.]
-
A.
isUsedInPhrase
Indicates that something (such as a word, expression, or symbol) appears as a component within a particular phrase.
-
B.
includesSaying
chosen
Indicates that one entity (such as a text, speech, or communication) contains or incorporates a particular saying, phrase, or quoted expression.
-
C.
titleContainsVerb
Indicates that the title of an entity includes at least one verb within its text.
-
D.
spellingIncludes
Indicates that the spelling of one entity contains, as a substring or component, the spelling of another entity.
-
E.
containsText
Indicates that one entity includes the specified text string within its content.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03686bfb8819080ba0caae652170a |
completed | April 16, 2026, 1:08 a.m. |
| PD | Predicate disambiguation | batch_69deca935e2c8190b640987ddfc542b9 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:15 a.m.