Triple
T25858369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Sufi Path of Knowledge |
E651412
|
entity |
| Predicate | analyzesText |
P170
|
FINISHED |
| Object | al-Futūḥāt al-Makkiyya |
—
|
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: al-Futūḥāt al-Makkiyya | Statement: [The Sufi Path of Knowledge, analyzesText, al-Futūḥāt al-Makkiyya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: analyzesText Context triple: [The Sufi Path of Knowledge, analyzesText, al-Futūḥāt al-Makkiyya]
-
A.
textualCharacterization
Indicates that one entity provides a descriptive or narrative characterization of another entity, typically in textual form.
-
B.
helpsAnalyze
Indicates that one entity assists another in examining, interpreting, or understanding something in a more detailed or effective way.
-
C.
analyzes
chosen
Indicates that one entity systematically examines or evaluates another entity to understand its nature, structure, or components.
-
D.
analysisType
Indicates the specific kind or category of analysis being applied or performed in relation to an entity or dataset.
-
E.
contentCharacterization
Indicates that one entity characterizes, describes, or classifies the content or informational nature of another entity.
- 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_69e7ab39035c8190be15c8aaee1bb858 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f60269c0fc8190a73fa347f8d99281 |
completed | May 2, 2026, 1:55 p.m. |
| PD | Predicate disambiguation | batch_69f5afec3e94819080d9ba86cf8c866e |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 22, 2026, 8:01 a.m.