Triple
T20185881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muuga Harbour |
E492857
|
entity |
| Predicate | nearSettlement |
P3883
|
FINISHED |
| Object | Maardu |
—
|
NE NERFINISHED |
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: Maardu | Statement: [Muuga Harbour, nearSettlement, Maardu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maardu Context triple: [Muuga Harbour, nearSettlement, Maardu]
-
A.
Maardu
chosen
Maardu is an industrial town in northern Estonia, located just east of the capital Tallinn in Harju County.
-
B.
Haapsalu
Haapsalu is a small seaside town in western Estonia known for its historic wooden architecture, medieval castle, and traditional seaside resort and spa culture.
-
C.
Pärnu
Pärnu is a coastal city in southwestern Estonia known as a popular summer resort and spa destination on the Baltic Sea.
-
D.
Tallinn
Tallinn is the capital and largest city of Estonia, a historic Baltic Sea port known for its well-preserved medieval Old Town and strategic maritime location.
-
E.
Tartu
Tartu is Estonia’s second-largest city and a historic cultural and intellectual center, best known as the country’s main university town.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e668f2c03c819096336462f59dba91 |
completed | April 20, 2026, 5:57 p.m. |
Created at: April 11, 2026, 11:36 p.m.