Triple
T25332691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lunglei |
E635192
|
entity |
| Predicate | secondLargestTownIn |
P170331
|
FINISHED |
| Object | Mizoram |
—
|
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: Mizoram | Statement: [Lunglei, secondLargestTownIn, Mizoram]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondLargestTownIn Context triple: [Lunglei, secondLargestTownIn, Mizoram]
-
A.
secondLargestVillage
Indicates that one village is ranked as the second largest in size or population within a specified set, region, or context.
-
B.
secondLargestMetropolitanArea
Indicates that one entity is the second largest metropolitan area (by population or size, as context defines) within the scope defined by the other entity.
-
C.
secondMetropolitan
Indicates that one entity is the second metropolitan (e.g., second-ranking or second-designated metropolitan authority or see) in relation to another entity.
-
D.
secondLargestUNCentreAfter
Indicates that one entity is the second-largest United Nations centre following another specified UN centre in terms of size or importance.
-
E.
secondLargestStructureIn
Indicates that one structure is the second largest (by size, height, volume, or another defined measure) among all structures within a specified location or context.
- F. None of above. chosen
Provenance (4 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_69e75a9908108190a95427a97020632a |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f69063edbc81909e7735954aabee0b |
completed | May 3, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_69f68b78f29481908cc8f390496dee97 |
completed | May 2, 2026, 11:40 p.m. |
| PDg | Predicate description generation | batch_69f68f6584a88190a8c4d95c0c84bee9 |
completed | May 2, 2026, 11:57 p.m. |
Created at: April 21, 2026, 1:30 p.m.