Triple
T860459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | War and Peace |
E18587
|
entity |
| Predicate | settingPlace |
P1957
|
FINISHED |
| Object | St. Petersburg |
E916
|
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: St. Petersburg | Statement: [War and Peace, settingPlace, St. Petersburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: St. Petersburg Context triple: [War and Peace, settingPlace, St. Petersburg]
-
A.
St. Petersburg
chosen
St. Petersburg is a major Russian port city on the Baltic Sea, renowned for its imperial architecture, cultural heritage, and role as a historic capital of Russia.
-
B.
St. Petersburg, Florida
St. Petersburg, Florida is a coastal city on Florida’s Gulf Coast known for its sunny climate, beaches, and vibrant arts and cultural scene.
-
C.
Pushkino
Pushkino is a town in Russia that serves as a suburban residential and industrial center northeast of Moscow.
-
D.
Samara
Samara is a major Russian city on the Volga River known as an important industrial, cultural, and transportation hub.
-
E.
Petergof
Petergof is a historic Russian town near Saint Petersburg, renowned for its grand imperial palaces, elaborate fountains, and landscaped parks along the Gulf of Finland.
- 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac52536c8190ab198232e9ec4bd6 |
completed | March 1, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac427fffe88190b28bd1b660bb90fe |
completed | March 7, 2026, 3:21 p.m. |
Created at: March 1, 2026, 7:39 p.m.