Triple
T14012232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weligama railway station |
E337110
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Weligama |
E321674
|
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: Weligama | Statement: [Weligama railway station, locatedIn, Weligama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weligama Context triple: [Weligama railway station, locatedIn, Weligama]
-
A.
Weligama
chosen
Weligama is a coastal town in southern Sri Lanka known for its scenic bay, surfing spots, and traditional stilt fishing.
-
B.
Hikkaduwa
Hikkaduwa is a coastal town in southern Sri Lanka known for its beaches, coral reefs, and surf culture.
-
C.
Kaduwela
Kaduwela is a rapidly developing suburban town in Sri Lanka’s Western Province, situated near Colombo and known for its growing residential and commercial significance.
-
D.
Bambalapitiya
Bambalapitiya is a coastal suburb and prominent residential and commercial neighborhood in Colombo, Sri Lanka.
-
E.
Maradana
Maradana is a densely populated, centrally located neighborhood in Colombo, Sri Lanka, known as a major transport and educational hub of the city.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed5cfd0819085b9c860b119a9de |
completed | April 14, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbacaa16e88190995fd86951fb54e6 |
completed | May 6, 2026, 9:03 p.m. |
Created at: April 9, 2026, 10:19 p.m.