Triple
T21628058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manipal |
E533754
|
entity |
| Predicate | hasCampusTownCharacter |
P145292
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Manipal, hasCampusTownCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCampusTownCharacter Context triple: [Manipal, hasCampusTownCharacter, true]
-
A.
hasCampusCharacter
Indicates that an institution possesses a particular overall campus atmosphere, style, or defining quality.
-
B.
hasCampusCity
Indicates that an educational institution or campus is located in a particular city.
-
C.
hasCampusOn
Indicates that an institution or organization maintains a campus located on a specified geographic area or site.
-
D.
hasCampusFeature
Indicates that a campus possesses or includes a specific physical or functional feature.
-
E.
hasCollegeTown
Indicates that a college or university is associated with, or located in, a particular town that serves as its college town.
- 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_69e0c464fba881908d0ff2ac80511ce1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef5214e72c8190af109089f58fca4f |
completed | April 27, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69e69677b9c48190bf81f795aa8ad74e |
completed | April 20, 2026, 9:11 p.m. |
| PDg | Predicate description generation | batch_69e69cb4bcbc8190a4fc2d508df107be |
completed | April 20, 2026, 9:37 p.m. |
Created at: April 16, 2026, 6:34 p.m.