Triple
T29815856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abu al-Hussein al-Husseini al-Qurashi |
E757101
|
entity |
| Predicate | usesKunya |
P167898
|
FINISHED |
| Object | Abu al-Hussein |
—
|
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: Abu al-Hussein | Statement: [Abu al-Hussein al-Husseini al-Qurashi, usesKunya, Abu al-Hussein]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesKunya Context triple: [Abu al-Hussein al-Husseini al-Qurashi, usesKunya, Abu al-Hussein]
-
A.
usesLanguageFor
Indicates that an entity employs a particular language as a tool or medium to perform some activity, function, or purpose.
-
B.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
C.
usedCulture
Indicates that one entity employed, applied, or drew upon the cultural practices, norms, or artifacts associated with another entity.
-
D.
usesColloquialCharacters
Indicates that an expression, name, or text is written using informal, non-standard, or colloquial characters rather than formal or standard script.
-
E.
hasEndonym
Indicates that an entity has a name or designation used by native speakers or within its own local language or community.
- 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_69f2245701c88190ad42415a0956c4ed |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f675637b0c81908fca0623b5feb312 |
completed | May 2, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66c59de9881909ebbb7b0ae7ab495 |
completed | May 2, 2026, 9:27 p.m. |
Created at: April 29, 2026, 5:26 p.m.