Triple
T8771594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiraspol |
E208474
|
entity |
| Predicate | hasRailConnectionTo |
P848
|
FINISHED |
| Object | Chisinau |
E134770
|
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: Chisinau | Statement: [Tiraspol, hasRailConnectionTo, Chisinau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chisinau Context triple: [Tiraspol, hasRailConnectionTo, Chisinau]
-
A.
Chișinău
chosen
Chișinău is the largest city and main political, economic, and cultural center of Moldova.
-
B.
Tiraspol
Tiraspol is the de facto capital and largest city of the unrecognized breakaway region of Transnistria in eastern Moldova.
-
C.
Minsk
Minsk is the capital and largest city of Belarus, serving as its political, economic, and cultural center.
-
D.
Beltsy, Moldova
Beltsy, Moldova is a major industrial and cultural city in northern Moldova, often considered the country’s second-largest urban center.
-
E.
Bălți
Bălți is a major city in northern Moldova, known as an important industrial, cultural, and transportation center often referred to as the country’s "northern capital."
- 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_69ca835edb4481909b4aafb616dc5eb7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f2c54c08190a904723d1f0527a4 |
completed | March 31, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf51b7d05c8190b84e02a8796d3422 |
completed | April 3, 2026, 5:35 a.m. |
Created at: March 30, 2026, 6:41 p.m.