Triple
T7788372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amaravati |
E187306
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Guntur |
E35413
|
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: Guntur | Statement: [Amaravati, nearCity, Guntur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guntur Context triple: [Amaravati, nearCity, Guntur]
-
A.
Guntur
chosen
Guntur is a major city in the Indian state of Andhra Pradesh, known historically as an important administrative and commercial center in southeastern India.
-
B.
Kakinada
Kakinada is a coastal city in the Indian state of Andhra Pradesh, known for its port, seafood industry, and role as a regional commercial hub.
-
C.
Khammam
Khammam is a city in the Indian state of Telangana known for its historical forts, temples, and proximity to the Munneru River.
-
D.
Nellore
Nellore is a prominent coastal city in southeastern India known for its agriculture, aquaculture, and role as an administrative and commercial center in Andhra Pradesh.
-
E.
Nandyal
Nandyal is a city in the Indian state of Andhra Pradesh, known as a commercial and administrative center in the Rayalaseema region.
- 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_69ca82af2d2c8190963861f5e0b8bf21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cae7e8a0d08190b6d4ca560681eb35 |
completed | March 30, 2026, 9:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb139fdf188190b1c80dd6d008c34d |
completed | March 31, 2026, 12:21 a.m. |
Created at: March 30, 2026, 4:24 p.m.