Triple
T1525336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Am Malala |
E32322
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Mingora |
E34338
|
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: Mingora | Statement: [I Am Malala, setting, Mingora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mingora Context triple: [I Am Malala, setting, Mingora]
-
A.
Mingora
chosen
Mingora is the largest city in Pakistan’s Swat Valley, known as a commercial and tourist hub and as the hometown of Nobel laureate Malala Yousafzai.
-
B.
Swabi
Swabi is a city in northern Pakistan known as an agricultural and commercial center in the Khyber Pakhtunkhwa province.
-
C.
Bannu
Bannu is a historic city in northwestern Pakistan known as a regional commercial and cultural center in the Khyber Pakhtunkhwa province.
-
D.
Umarkot
Umarkot is a historic town in the Sindh province of Pakistan, traditionally known as the birthplace of the Mughal emperor Akbar.
-
E.
Nawabshah
Nawabshah is a major city in Pakistan known as an important commercial and agricultural center in the Sindh province.
- 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa61f7bb60819094774ecc632255de |
completed | March 6, 2026, 5:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad401616ec81908edd9dcb9f4a0184 |
completed | March 8, 2026, 9:23 a.m. |
Created at: March 4, 2026, 7:26 p.m.