Triple
T14667852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ulsoor |
E344425
|
entity |
| Predicate | hasOfficialName |
P66
|
FINISHED |
| Object | Halasuru |
E342837
|
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: Halasuru | Statement: [Ulsoor, hasOfficialName, Halasuru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halasuru Context triple: [Ulsoor, hasOfficialName, Halasuru]
-
A.
Halasuru
chosen
Halasuru is a historic neighborhood in eastern Bengaluru, India, known for its temples, markets, and proximity to Ulsoor Lake.
-
B.
Nayandahalli
Nayandahalli is a locality in southwestern Bangalore known as a key junction and residential area along major transport routes.
-
C.
Harohalli
Harohalli is a town in the Indian state of Karnataka, known for its industrial area and proximity to Bengaluru.
-
D.
Hesaraghatta
Hesaraghatta is a locality near Bengaluru in Karnataka, India, known for its lake, grasslands, and role as a water source and ecological zone for the region.
-
E.
Devanahalli
Devanahalli is a town near Bengaluru in the Indian state of Karnataka, notable for its rapid development and proximity to Kempegowda International Airport.
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb54dda1c8190bf16d17e26a2bba6 |
completed | April 14, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde177ced48190a448cbee1f4c75bf |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:27 a.m.