Triple
T38204408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Speeches and Table-Talk of the Prophet Mohammad |
E1009156
|
entity |
| Predicate | translatorOrInterpreter |
P5475
|
FINISHED |
| Object | Stanley Lane-Poole |
—
|
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: Stanley Lane-Poole | Statement: [The Speeches and Table-Talk of the Prophet Mohammad, translatorOrInterpreter, Stanley Lane-Poole]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: translatorOrInterpreter Context triple: [The Speeches and Table-Talk of the Prophet Mohammad, translatorOrInterpreter, Stanley Lane-Poole]
-
A.
translator
chosen
Indicates that one entity serves to convert or render content from one language or form into another for a second entity.
-
B.
languageOfInterpretation
Indicates the language in which something (such as text, speech, or content) is interpreted or understood.
-
C.
interpreter
Indicates that one entity serves to translate or render the meaning of another entity (such as language, code, or symbols) into an understandable or executable form.
-
D.
translationActivity
Indicates that an entity is engaged in the process of translating content from one language or form into another.
-
E.
textTranslation
Indicates a relationship where one text is rendered into another language or form while preserving its original meaning.
- 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_69f76dc94fcc8190bd2f55e81f9d6527 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fff09dae088190bd8460060d778feb |
completed | May 10, 2026, 2:42 a.m. |
| PD | Predicate disambiguation | batch_69fff0027c5c8190baa5c7a15852cbe0 |
completed | May 10, 2026, 2:40 a.m. |
Created at: May 3, 2026, 4:30 p.m.