Triple
T14430925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ladyfinger Peak |
E357824
|
entity |
| Predicate | visibilityFrom |
P2524
|
FINISHED |
| Object | Karimabad town |
E271539
|
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: Karimabad town | Statement: [Ladyfinger Peak, visibilityFrom, Karimabad town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karimabad town Context triple: [Ladyfinger Peak, visibilityFrom, Karimabad town]
-
A.
Karimabad
Karimabad is a neighborhood in Karachi, Pakistan, known for its bustling markets and central urban location within the city.
-
B.
Karimabad
chosen
Karimabad is a picturesque town in northern Pakistan’s Hunza region, known for its stunning mountain scenery, historic forts, and role as a popular base for trekkers and tourists.
-
C.
Yaseenabad
Yaseenabad is a residential neighborhood located within the Federal B Area of Karachi, Pakistan.
-
D.
Jalalpur
Jalalpur is a town and urban center located in the Ambedkar Nagar district of Uttar Pradesh, India.
-
E.
Jauharabad
Jauharabad is a planned town in Pakistan’s Punjab province, known for its proximity to key industrial and strategic facilities.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de914570f08190b1c7c1c57a0cb476 |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94a254f881908d9494d4602064ae |
completed | May 8, 2026, 7:45 a.m. |
Created at: April 10, 2026, 1:18 a.m.