Triple
T6276977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quentin Roosevelt Boulevard |
E140684
|
entity |
| Predicate | hasEponymRole |
P53948
|
FINISHED |
| Object | United States Army Air Service officer |
—
|
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: United States Army Air Service officer | Statement: [Quentin Roosevelt Boulevard, hasEponymRole, United States Army Air Service officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEponymRole Context triple: [Quentin Roosevelt Boulevard, hasEponymRole, United States Army Air Service officer]
-
A.
hasEponymConnectionTo
Indicates that one entity is named after, derived from, or otherwise linguistically or honorifically connected to another entity as its eponym.
-
B.
isNamedForEponymRole
chosen
Indicates that one entity bears a name derived from another entity that serves as its eponym or namesake.
-
C.
hasFamousNamesakeRole
Indicates that an entity has a role or position that shares its name with a well-known or historically notable person.
-
D.
eponymFor
Indicates that one entity gives its name to another entity, which is then named after it.
-
E.
namedPersonRole
Indicates that a person is identified by name as holding a specific role or position in a given context.
- 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_69c008cc158881908df6ec94a911c736 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063d96fbc8190a9091456b82762d1 |
completed | March 22, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69c05608a5608190b22a1fdc4060470d |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:26 p.m.