Triple
T28173802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston University crew teams |
E715533
|
entity |
| Predicate | trainOn |
P20525
|
FINISHED |
| Object | Charles River |
—
|
NE NERFINISHED |
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: Charles River | Statement: [Boston University crew teams, trainOn, Charles River]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainOn Context triple: [Boston University crew teams, trainOn, Charles River]
-
A.
trainingUse
chosen
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
B.
trainingIn
Indicates that one entity is undergoing or receiving training within the context, program, or domain specified by another entity.
-
C.
trainingUnder
Indicates that one entity is receiving instruction, guidance, or mentorship from another, typically in a subordinate or apprentice-like capacity.
-
D.
trainedNear
Indicates that one entity received training at a location that is geographically close to another specified entity or location.
-
E.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
- 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_69efd6b340f0819095680e15dcdc1830 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f6659b62fc8190b21555d0ba54db2d |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 27, 2026, 10:14 p.m.