Triple
T5315703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyderabad (Sindh) |
E119141
|
entity |
| Predicate | rankInPopulationInSindh |
P50047
|
FINISHED |
| Object | second-largest city after Karachi |
—
|
LITERAL 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: second-largest city after Karachi | Statement: [Hyderabad (Sindh), rankInPopulationInSindh, second-largest city after Karachi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInPopulationInSindh Context triple: [Hyderabad (Sindh), rankInPopulationInSindh, second-largest city after Karachi]
-
A.
populationRankInSindh
chosen
Indicates the relative position of an entity in terms of population size compared to other entities within Sindh.
-
B.
areaRankInPakistan
Indicates the relative position of an entity when all entities in Pakistan are ordered by their area size.
-
C.
hasPopulationRankInRegion
Indicates that an entity has a specific population-based rank or position within a defined geographic region.
-
D.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
E.
rankByHeightPakistan
Indicates an ordering of entities based on their height specifically within the context of Pakistan.
- 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_69bd446b57bc8190a513d2e6c40314f3 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd86f20f008190be7b5848af05f2b8 |
completed | March 20, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69bd84534f9c8190bc19d4812060768d |
completed | March 20, 2026, 5:30 p.m. |
Created at: March 20, 2026, 1:54 p.m.