Triple
T510567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anthony Wayne |
E10596
|
entity |
| Predicate | hasPartInMotto |
P14561
|
FINISHED |
| Object | “Legion of the United States” professional standing army reforms |
—
|
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: “Legion of the United States” professional standing army reforms | Statement: [Anthony Wayne, hasPartInMotto, “Legion of the United States” professional standing army reforms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPartInMotto Context triple: [Anthony Wayne, hasPartInMotto, “Legion of the United States” professional standing army reforms]
-
A.
hasMottoRibbon
Indicates that an entity features or is associated with a ribbon element specifically used to display a motto.
-
B.
mottoType
Indicates the specific category or kind of motto that characterizes the relationship between an entity and its motto.
-
C.
motto
Indicates that one entity serves as the guiding phrase, slogan, or maxim associated with another entity.
-
D.
scriptUsedForMotto
Indicates that a particular writing system or script is used to render or express a given motto.
-
E.
mottoOriginal
Indicates that one entity is the original wording or form of another entity’s motto.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f165b91c81908c2d2ba15c64b956 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edfe236481909901cc7d4281b33c |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eebbd70481908b462296671de67b |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.