Triple
T25493323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yusuf ibn Ya‘qub |
E638890
|
entity |
| Predicate | correspondingNameInEnglish |
P55633
|
FINISHED |
| Object | Joseph |
—
|
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: Joseph | Statement: [Yusuf ibn Ya‘qub, correspondingNameInEnglish, Joseph]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: correspondingNameInEnglish Context triple: [Yusuf ibn Ya‘qub, correspondingNameInEnglish, Joseph]
-
A.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
-
B.
equivalentGivenNameInEnglish
Indicates that two given names are equivalent in meaning or usage when expressed in English.
-
C.
hasOfficialNameInEnglish
Indicates that an entity has an officially recognized name expressed in the English language.
-
D.
counterpartEnglishName
chosen
Indicates that an entity has a corresponding counterpart whose name is given in English.
-
E.
hasEnglishNameMeaning
Indicates that an entity is associated with an English-language name along with the meaning or semantic interpretation of that name.
- 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_69e75dbbd2a88190b70e1e645de14b9a |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f606c79ad081908369605f72e65ca6 |
completed | May 2, 2026, 2:14 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 21, 2026, 2:39 p.m.