Triple
T6702648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harvard-Westlake School |
E152917
|
entity |
| Predicate | campusStructure |
P72522
|
FINISHED |
| Object | multiple campuses |
—
|
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 campuses | Statement: [Harvard-Westlake School, campusStructure, multiple campuses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: campusStructure Context triple: [Harvard-Westlake School, campusStructure, multiple campuses]
-
A.
campusLandmark
Indicates that something serves as a notable or recognizable landmark located on or associated with a campus.
-
B.
campusFacility
Indicates that one entity is a facility that is located on or belongs to a particular campus.
-
C.
cityCampus
Indicates that a campus is located within or associated with a particular city.
-
D.
campusQuadrant
Indicates the specific sector or quadrant of a campus in which an entity is located or to which it belongs.
-
E.
campusArea
Indicates that one entity is the physical area or spatial extent of a campus associated with another entity.
- 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_69c68807adbc8190b8632df42b39eda0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16897e48190b43eda2206b14d6a |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d089c7488190a00853fb12f53b2a |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d1668a7c8190ae93951f9ba2df10 |
completed | March 27, 2026, 6:50 p.m. |
Created at: March 27, 2026, 2:06 p.m.