Triple
T11388147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Sutton |
E269760
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Rawalpindi |
E35027
|
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: Rawalpindi | Statement: [John Sutton, placeOfBirth, Rawalpindi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rawalpindi Context triple: [John Sutton, placeOfBirth, Rawalpindi]
-
A.
Rawalpindi
chosen
Rawalpindi is a major city in Pakistan’s Punjab province, historically significant as a former temporary national capital and now a key commercial and military center.
-
B.
Lahore
Lahore is a major cultural, historical, and economic center of Pakistan, known for its rich Mughal heritage, educational institutions, and role in the region’s political history.
-
C.
Islamabad
Islamabad is Pakistan’s planned, modern capital city known for its high standard of living, greenery, and role as the country’s political and administrative center.
-
D.
Peshawar
Peshawar is one of Pakistan’s oldest and largest cities, a historic cultural and economic hub located near the Khyber Pass in the country’s northwest.
-
E.
Faisalabad
Faisalabad is a major industrial city in Pakistan’s Punjab province, known especially for its large textile industry and role as a commercial hub.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7fc389d4c81909515a5c8b0099c36 |
completed | April 9, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e684d1bcd08190925cc9a2e844c900 |
completed | April 20, 2026, 7:56 p.m. |
Created at: April 8, 2026, 9:34 p.m.