Triple
T12709480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoover family |
E303678
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Hoover |
E16693
|
NE 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: Hoover | Statement: [Hoover family, hasSurname, Hoover]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hoover Context triple: [Hoover family, hasSurname, Hoover]
-
A.
Hoover
chosen
Hoover is a surname most prominently associated with Herbert Hoover, the 31st president of the United States.
-
B.
Hoover
Hoover is a suburban city in the Birmingham metropolitan area of central Alabama, known for its residential communities and shopping centers like the Riverchase Galleria.
-
C.
Hoovers
Hoovers are British Rail Class 50 diesel-electric locomotives, informally named for the distinctive vacuum-cleaner-like sound of their original cooling fans.
-
D.
Otis
Otis is a globally recognized manufacturer of elevators, escalators, and moving walkways, known for pioneering vertical transportation technologies.
-
E.
Otis
Otis is a small rural town in western Massachusetts known for its forests, lakes, and outdoor recreation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96207b2d881908314efc3e350aa78 |
completed | April 10, 2026, 8:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671b648f48190a29924c484713fc7 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:23 p.m.