Triple
T21478567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karachi–Peshawar Railway Line |
E529925
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Wazirabad |
—
|
NE NERFINISHED |
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: Wazirabad | Statement: [Karachi–Peshawar Railway Line, connectsCity, Wazirabad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wazirabad Context triple: [Karachi–Peshawar Railway Line, connectsCity, Wazirabad]
-
A.
Wazirabad
chosen
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
-
B.
Jauharabad
Jauharabad is a planned town in Pakistan’s Punjab province, known for its proximity to key industrial and strategic facilities.
-
C.
Haroonabad
Haroonabad is a town in Pakistan known for its agricultural surroundings and role as a local commercial center.
-
D.
Shahabad
Shahabad is a town in the Kurukshetra district of Haryana, India, known historically by its former name Shahbad Markanda.
-
E.
Shahabad
Shahabad is a town in Uttar Pradesh, India, situated within the administrative boundaries of Rampur district.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c45acc3881908e38d3f28964152b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea1951fc8190910f634327aa5c3f |
completed | April 23, 2026, 9:44 a.m. |
Created at: April 16, 2026, 6:20 p.m.