Triple
T15809870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vikarabad district |
E383316
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Vikarabad |
E1094135
|
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: Vikarabad | Statement: [Vikarabad district, hasCity, Vikarabad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vikarabad Context triple: [Vikarabad district, hasCity, Vikarabad]
-
A.
Vikarabad
chosen
Vikarabad is a town in the Indian state of Telangana known for its nearby Ananthagiri Hills, a popular hill station and trekking destination.
-
B.
Nasirabad
Nasirabad is a town and administrative area located in the Balochistan region of present-day Pakistan.
-
C.
Nasirabad
Nasirabad is a village in the Lower Hunza region of northern Pakistan, known for its mountainous terrain and proximity to the Karakoram Range.
-
D.
Shamshabad
Shamshabad is a suburban area near Hyderabad in the Indian state of Telangana, known primarily for hosting the Rajiv Gandhi International Airport.
-
E.
Nooriabad
Nooriabad is an industrial town in the Jamshoro District of Sindh, Pakistan, known for its manufacturing zones and proximity to Karachi.
- 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b529c6b481909664153ecc381f7c |
completed | April 16, 2026, 10:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe66d78c81908308fc16c8d4e19c |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:49 a.m.