Triple
T15809871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vikarabad district |
E383316
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Tandur |
E1089780
|
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: Tandur | Statement: [Vikarabad district, hasTown, Tandur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tandur Context triple: [Vikarabad district, hasTown, Tandur]
-
A.
Tandur
chosen
Tandur is a town in the Indian state of Telangana known for its limestone industries and stone quarries.
-
B.
Tordino
Tordino is a river in the Abruzzo region of central Italy that flows through the city of Teramo before reaching the Adriatic Sea.
-
C.
Tunasan
Tunasan is a barangay and district in the southern part of Muntinlupa City in Metro Manila, Philippines.
-
D.
Tendaba
Tendaba is a small riverside village in The Gambia known as a key gateway and base for visiting Kiang West National Park and its surrounding wildlife areas.
-
E.
Tarusa
Tarusa is a small historic town in western Russia known for its scenic location on the Oka River and its associations with Russian artists and writers.
- 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_69ff999210148190baa6dcb19be3a1d3 |
completed | May 9, 2026, 8:31 p.m. |
Created at: April 10, 2026, 4:49 a.m.