Triple
T27696617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lal Pur District |
E698311
|
entity |
| Predicate | borderingProvinceAcrossBorder |
P32798
|
FINISHED |
| Object | Khyber Pakhtunkhwa |
—
|
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: Khyber Pakhtunkhwa | Statement: [Lal Pur District, borderingProvinceAcrossBorder, Khyber Pakhtunkhwa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderingProvinceAcrossBorder Context triple: [Lal Pur District, borderingProvinceAcrossBorder, Khyber Pakhtunkhwa]
-
A.
provinceBordering
chosen
Indicates that two provinces share a common boundary or border with each other.
-
B.
borderedBy
Indicates that one entity shares a common boundary or edge with another entity.
-
C.
borderingCountryOnOtherSide
Indicates that one country lies on the opposite side of a shared border relative to another country.
-
D.
oppositeTownAcrossBorder
Indicates that one town is located directly across a border from another town, positioned as its opposite counterpart.
-
E.
countyBorder
Indicates that two counties share a common boundary or border with each other.
- F. None of above.
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_69ef590ea74081908f0cd7500d85fa27 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69fd02680d948190a3463fb119ba8556 |
completed | May 7, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69fcf89c69b4819082bbc564bd15137d |
completed | May 7, 2026, 8:39 p.m. |
Created at: April 27, 2026, 2:54 p.m.