Triple
T15373248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Permic script |
E367600
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Perm |
E129564
|
NE 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: Perm | Statement: [Old Permic script, namedAfter, Perm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Perm Context triple: [Old Permic script, namedAfter, Perm]
-
A.
Perm
chosen
Perm is a major industrial and cultural city in the Ural region of Russia, situated on the Kama River and historically significant as a gateway between European and Asian Russia.
-
B.
Permet
Permet is a small town in southern Albania known for its scenic location along the Vjosa River, thermal springs, and surrounding mountainous landscapes.
-
C.
PERMIS
PERMIS is the Permanent International Secretariat of the Black Sea Economic Cooperation (BSEC), serving as its main administrative and coordinating body.
-
D.
PERM
PERM is the U.S. Department of Labor’s permanent labor certification process that employers must complete to hire foreign workers for certain employment-based green card categories.
-
E.
Per
Per is a Scandinavian masculine given name, commonly used in Norway, Sweden, and Denmark as a form of Peter.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e5c1d548190930bfaf0861595ae |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1347c8448190aa1088d66bca2722 |
completed | May 9, 2026, 10:58 a.m. |
Created at: April 10, 2026, 3:18 a.m.