Triple
T176339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portuguese language |
E3583
|
entity |
| Predicate | spokenIn |
P2266
|
FINISHED |
| Object | Goa |
E9427
|
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: Goa | Statement: [Portuguese language, spokenIn, Goa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goa Context triple: [Portuguese language, spokenIn, Goa]
-
A.
Goa
chosen
Goa is a coastal state on India’s western shore known for its beaches, distinctive blend of Indian and Portuguese heritage, and vibrant tourism industry.
-
B.
Gujarat
Gujarat is a western coastal state of India known for its significant role in trade and industry, rich cultural heritage, and historic cities such as Ahmedabad.
-
C.
Maharashtra
Maharashtra is a large and economically significant state in western India, known for its capital Mumbai, the country’s financial hub, and its rich cultural and historical heritage.
-
D.
Rajasthan
Rajasthan is a northwestern Indian state known for its vast Thar Desert, historic Rajput forts and palaces, and rich cultural heritage.
-
E.
Assam
Assam is a northeastern region of the Indian subcontinent known for its tea plantations, rich biodiversity, and distinct cultural heritage.
- 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_69a25374990081909766d30c79a18e0e |
completed | Feb. 28, 2026, 2:31 a.m. |
| NER | Named-entity recognition | batch_69a258e497788190aeb61d981efb4d1d |
completed | Feb. 28, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a332c5dd9c8190af9debeb3fae0ff0 |
completed | Feb. 28, 2026, 6:24 p.m. |
Created at: Feb. 28, 2026, 2:39 a.m.