Triple
T29826856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle for the Fremont Cannon |
E757402
|
entity |
| Predicate | trophyColorCoding |
P41239
|
FINISHED |
| Object | painted in winner’s school colors |
—
|
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: painted in winner’s school colors | Statement: [Battle for the Fremont Cannon, trophyColorCoding, painted in winner’s school colors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trophyColorCoding Context triple: [Battle for the Fremont Cannon, trophyColorCoding, painted in winner’s school colors]
-
A.
trophyColor
chosen
Indicates the color attribute assigned to a trophy in the relationship.
-
B.
trophyCount
Indicates the number of trophies associated with a given entity.
-
C.
trophyNameRefersTo
Indicates that a given trophy name refers to, denotes, or is associated with a particular trophy entity.
-
D.
trophyCategory
Indicates the classification or type of award or trophy that an entity is associated with.
-
E.
trophyFeatures
Indicates that a trophy possesses or is characterized by certain features or attributes.
- 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_69f22457c84c8190a6d9f56bc74082a9 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67598dff081908ff0ec79a48b55ec |
completed | May 2, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69f66ac32b60819092290b2de35988d3 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 29, 2026, 5:32 p.m.