Triple
T15067063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chandka Medical College |
E379782
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Larkana |
E77818
|
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: Larkana | Statement: [Chandka Medical College, locatedIn, Larkana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Larkana Context triple: [Chandka Medical College, locatedIn, Larkana]
-
A.
Larkana
chosen
Larkana is a major city in Pakistan known for its historical significance, including proximity to the ancient Indus Valley site of Mohenjo-daro and its association with the Bhutto political family.
-
B.
Shakardara
Shakardara is a town and administrative settlement in Pakistan’s Khyber Pakhtunkhwa province, known for its role within the Kohat region.
-
C.
Khar
Khar is a suburban neighborhood in Mumbai, India, known for its residential areas, shopping streets, and proximity to the Arabian Sea.
-
D.
Khar
Khar is a town in northwestern Pakistan that serves as the administrative and commercial center of the Bajaur region in Khyber Pakhtunkhwa.
-
E.
Tarkarli
Tarkarli is a coastal village in Maharashtra, India, known for its pristine beaches, clear waters, and popular scuba diving and snorkeling spots.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedeea750c819082d8823c9ab6c5a2 |
completed | April 15, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5e2b2188190b96807ca6442fb01 |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 3:02 a.m.