Triple
T13415509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faridkot district |
E313200
|
entity |
| Predicate | hasHistoricTown |
P847
|
FINISHED |
| Object | Faridkot |
E313199
|
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: Faridkot | Statement: [Faridkot district, hasHistoricTown, Faridkot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Faridkot Context triple: [Faridkot district, hasHistoricTown, Faridkot]
-
A.
Faridkot
chosen
Faridkot is a historic town and district headquarters in the Malwa region of Punjab, India, known for its cultural heritage and agricultural surroundings.
-
B.
Hoshiarpur
Hoshiarpur is a historic city in the Indian state of Punjab, known for its cultural heritage, educational institutions, and agricultural surroundings.
-
C.
Gurdaspur
Gurdaspur is a city in the northern Indian state of Punjab, known for its agricultural surroundings and proximity to the India–Pakistan border.
-
D.
Ferozepur
Ferozepur is a historic city in the Indian state of Punjab, known for its strategic location near the India–Pakistan border and its role in various military and independence-era events.
-
E.
Bathinda
Bathinda is a major city in southwestern Punjab, India, known as an important agricultural, industrial, and military center with historical forts and thermal power plants.
- 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_69d806ad0c44819088833ae1ec9e9690 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaeb6e904819098cc9153fd2feaf5 |
completed | April 12, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3cf863fc81908723f7f6b3d45510 |
completed | May 8, 2026, 1:31 a.m. |
Created at: April 9, 2026, 9:39 p.m.