Triple
T3796955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government College, Lahore |
E91592
|
entity |
| Predicate | countryRankReputation |
P45181
|
FINISHED |
| Object | among Pakistan’s most prestigious colleges |
—
|
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: among Pakistan’s most prestigious colleges | Statement: [Government College, Lahore, countryRankReputation, among Pakistan’s most prestigious colleges]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryRankReputation Context triple: [Government College, Lahore, countryRankReputation, among Pakistan’s most prestigious colleges]
-
A.
wealthRanking
Indicates the relative ordering of entities based on their level of wealth or financial resources.
-
B.
nationalReputation
Indicates the recognized standing or esteem an entity holds at the level of an entire nation.
-
C.
countryRanking
Indicates the relative position or rank assigned to a country within a specific ordered list or comparative evaluation.
-
D.
countryRankContext
Indicates the relative position or ranking of a country within a specified contextual framework (such as economic, political, or performance-based criteria).
-
E.
rankingInCountry
chosen
Indicates the position or level an entity holds within an ordered list specific to a particular country.
- 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_69aed96354f48190a768966d6bd19b04 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecefa3608190a7a20ed6df6a64b2 |
completed | March 9, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69aee743c8d08190a9f9c97b836bd703 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:15 p.m.