Triple
T8109261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prahova County |
E189305
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Băicoi |
E500879
|
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: Băicoi | Statement: [Prahova County, hasTown, Băicoi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Băicoi Context triple: [Prahova County, hasTown, Băicoi]
-
A.
Băicoi
chosen
Băicoi is a small industrial town in Prahova County, Romania, known for its oil industry and proximity to the city of Ploiești.
-
B.
Baiul
Baiul is the surname of Oksana Baiul, the Ukrainian figure skater who won the 1994 Olympic ladies' singles gold medal.
-
C.
Reșița
Reșița is an industrial city in western Romania, historically known as a major center of steel production and engineering in the Banat region.
-
D.
Crângași
Crângași is a residential neighborhood in western Bucharest, Romania, known for its large park and lakeside recreational areas along Lacul Morii.
-
E.
Giulești
Giulești is a residential neighborhood in western Bucharest, Romania, known for its working-class character and association with the Rapid București football club.
- 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_69ca82b9d5848190a24672775d5c5011 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb42fbc57c81908c6be87bbc547085 |
completed | March 31, 2026, 3:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbe994fc881908a43cfdf9f28753c |
completed | April 1, 2026, 6:43 a.m. |
Created at: March 30, 2026, 5:32 p.m.