Triple
T13652533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salil Parekh |
E326774
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Bengaluru |
E12663
|
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: Bengaluru | Statement: [Salil Parekh, workLocation, Bengaluru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bengaluru Context triple: [Salil Parekh, workLocation, Bengaluru]
-
A.
Bengaluru
chosen
Bengaluru is a major Indian metropolis known as the country’s leading technology and innovation hub, often called the “Silicon Valley of India.”
-
B.
Indiranagar
Indiranagar is a prominent residential and commercial neighborhood in eastern Bengaluru, known for its upscale eateries, pubs, and shopping streets.
-
C.
Mambai
Mambai is an Austronesian language spoken primarily in East Timor, where it is one of the country’s major indigenous languages.
-
D.
Hyderabad
Hyderabad is a major city in southern India known for its historic Charminar monument, rich Hyderabadi cuisine, and growing technology industry.
-
E.
Hyderabad
Hyderabad is a major city in the Sindh province of Pakistan, known for its historical significance, vibrant culture, and role as an important commercial and industrial center.
- 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_69d8076d8270819092afc2f0e9c359a8 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc609676c8190b5b1cabe6b315142 |
completed | April 12, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78ae1d1b08190ad07b159ac3eba4b |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:52 p.m.