Triple
T6545315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yavatmal |
E150991
|
entity |
| Predicate | hasColleges |
P72302
|
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: [Yavatmal, hasColleges, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasColleges Context triple: [Yavatmal, hasColleges, true]
-
A.
hasCollegesType
Indicates that an entity is associated with or classified by a particular type or category of college.
-
B.
hasAffiliatedCollegesIn
Indicates that an institution maintains affiliated colleges located within a specified geographic area or jurisdiction.
-
C.
hasUniversities
Indicates that an entity possesses, contains, or is associated with one or more universities.
-
D.
hasResidentialColleges
Indicates that an institution or organization includes one or more residential colleges as part of its structure or system.
-
E.
hasCollegeCampus
Indicates that an institution or organization possesses or is associated with a specific college campus as a physical or organizational site.
- 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_69c687f3fd60819083bfa583e5bcfa71 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ce07332481909a5a7964282eb776 |
completed | March 27, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69c6acf3e3708190b052ec774e607cb7 |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6ce0538f48190abf3160681901c17 |
completed | March 27, 2026, 6:35 p.m. |
Created at: March 27, 2026, 1:50 p.m.