Triple
T25959411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sri Chaitanya College, Hyderabad |
E645494
|
entity |
| Predicate | hasBranchesInCity |
P68855
|
FINISHED |
| Object | multiple locations in Hyderabad |
—
|
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: multiple locations in Hyderabad | Statement: [Sri Chaitanya College, Hyderabad, hasBranchesInCity, multiple locations in Hyderabad]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBranchesInCity Context triple: [Sri Chaitanya College, Hyderabad, hasBranchesInCity, multiple locations in Hyderabad]
-
A.
hasBranches
Indicates that an entity extends into multiple subordinate parts or offshoots, like limbs, divisions, or sections stemming from a main source.
-
B.
hasBranchOffice
chosen
Indicates that one organization maintains a branch office or subsidiary location in another place or entity.
-
C.
mayHaveBranchOfficesIn
Indicates that an entity is permitted or allowed to establish branch offices in a specified location.
-
D.
hasComponentCity
Indicates that an entity includes or is composed of one or more cities as its constituent parts.
-
E.
hasTargetCity
Indicates that something is directed toward, intended for, or specifically associated with a particular city as its target.
- 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_69e77e85efc08190997da7fcf98bd300 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f627aedf548190bc9f53c8a2d67b50 |
completed | May 2, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69f623a4e1048190bbb8dd1253fdcee9 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 22, 2026, 8:47 a.m.