Triple
T8790689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Venyovsky Uyezd |
E209155
|
entity |
| Predicate | governorateSeat |
P48989
|
FINISHED |
| Object | Tula |
E111344
|
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: Tula | Statement: [Venyovsky Uyezd, governorateSeat, Tula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tula Context triple: [Venyovsky Uyezd, governorateSeat, Tula]
-
A.
Tula
Tula is a town in the Logudoro region of northern Sardinia, Italy, known for its rural landscape and traditional Sardinian culture.
-
B.
Tula
chosen
Tula is a historic Russian city south of Moscow, known for its metalworking, samovar production, and as a cultural center near Leo Tolstoy’s estate at Yasnaya Polyana.
-
C.
Tula
Tula is an important ancient Mesoamerican city, once a major Toltec capital known for its monumental architecture and iconic stone warrior statues.
-
D.
Tula
Tula is the birth name of American actress and dancer Cyd Charisse, famed for her roles in classic Hollywood musicals.
-
E.
Tula
Tula is a small coastal village in the Eastern District of American Samoa known for its traditional Samoan culture and scenic Pacific island setting.
- 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_69ca836168108190bb43d3dc235c1f55 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f8d25f881908863d636fa57a8a2 |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf5220af84819095ddefd84fd9f369 |
completed | April 3, 2026, 5:37 a.m. |
Created at: March 30, 2026, 6:43 p.m.