Triple
T10402050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hank Bauer |
E245171
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Henry |
E254557
|
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: Henry | Statement: [Hank Bauer, givenName, Henry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Henry Context triple: [Hank Bauer, givenName, Henry]
-
A.
Henry
Henry is the given name of the influential American architect Henry Hobson Richardson, a key figure in 19th-century architecture.
-
B.
Henry
chosen
Henry is a masculine given name of Germanic origin that has been widely used by European royalty and notable historical figures.
-
C.
Henry
Henry is the given first name of American businessman and politician Ross Perot, who was a prominent third-party U.S. presidential candidate in the 1990s.
-
D.
Henry
Henry is the central protagonist of the video game "Gray Matter," around whom the story’s mystery and events revolve.
-
E.
Henry
Henry is the disturbed serial killer protagonist of the cult horror film "Henry: Portrait of a Serial Killer," portrayed by Michael Rooker.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9e42da08190a5383df3df6d3c18 |
completed | April 7, 2026, 11:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89f6fbc848190806d50bfad654b27 |
completed | April 10, 2026, 6:57 a.m. |
Created at: April 6, 2026, 12:08 p.m.