Triple
T18912917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumar Sangakkara |
E462650
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Matale |
—
|
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: Matale | Statement: [Kumar Sangakkara, placeOfBirth, Matale]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matale Context triple: [Kumar Sangakkara, placeOfBirth, Matale]
-
A.
Matale
chosen
Matale is a central Sri Lankan town known for its spice gardens, historical temples, and role in the island’s hill-country region.
-
B.
Sibaté
Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
-
C.
Gurabo
Gurabo is a municipality in eastern Puerto Rico known for its suburban character, scenic hills, and integration into the greater San Juan metropolitan region.
-
D.
Sutatausa
Sutatausa is a small municipality in Colombia’s Cundinamarca Department, known for its colonial heritage and scenic Andean landscapes.
-
E.
Samaniego
Samaniego is a surname of Spanish origin borne by various notable individuals, including figures in the arts, sports, and public life.
- 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_69d8dcfdbbb881909964fa5a75bd0b48 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c624516c81909e6bf04707d3c71c |
completed | April 20, 2026, 6:22 a.m. |
Created at: April 10, 2026, 11:58 a.m.