Triple
T15746495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Army units |
E381732
|
entity |
| Predicate | garrison |
P75
|
FINISHED |
| Object | Orel |
E191974
|
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: Orel | Statement: [Red Army units, garrison, Orel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orel Context triple: [Red Army units, garrison, Orel]
-
A.
Orel
Orel is a male given name most famously associated with former Major League Baseball pitcher Orel Hershiser.
-
B.
Ostan
Ostan was a historic city that served as the political and administrative center of the medieval Armenian region of Vaspurakan.
-
C.
Ngawi
Ngawi is a regency capital and regional town in East Java, Indonesia, known as a transportation hub and gateway between Central and East Java.
-
D.
Dunellen
Dunellen is a small borough in central New Jersey known for its residential character and commuter access to the New York metropolitan area.
-
E.
Orel salient
chosen
The Orel salient was a German-held bulge in the Eastern Front lines around the city of Orel in World War II, which became the focus of a major Soviet counteroffensive following the Battle of Kursk.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0502d72008190b4d13a6b3a12e467 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff8309cba881909579ee5a62b3aa31 |
completed | May 9, 2026, 6:55 p.m. |
Created at: April 10, 2026, 4:46 a.m.