Triple
T1795112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trinity School (New York City) |
E39584
|
entity |
| Predicate | hasCollegeCounseling |
P31996
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Trinity School (New York City), hasCollegeCounseling, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCollegeCounseling Context triple: [Trinity School (New York City), hasCollegeCounseling, yes]
-
A.
hasCollegeType
Indicates that a college or educational institution is classified as having a specific type or category (e.g., public, private, community, technical).
-
B.
connectsToCollege
Indicates a relationship where an entity has a direct link, affiliation, or pathway to a college.
-
C.
hasCollegeCampus
Indicates that an institution or organization possesses or is associated with a specific college campus as a physical or organizational site.
-
D.
collegeCoach
Indicates that one entity serves as a coach for a college-level sports team associated with another entity.
-
E.
numberOfColleges
Indicates the quantity of colleges associated with a given 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab61b6ea188190aab9fb839bf1e367 |
completed | March 6, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69aa61d2f7a8819090301f92d3e358c7 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab61b5c8988190bb2b46182a4eb5b4 |
completed | March 6, 2026, 11:22 p.m. |
Created at: March 4, 2026, 7:32 p.m.