Triple
T15808067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ansel Briggs |
E383272
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ansel |
E478445
|
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: Ansel | Statement: [Ansel Briggs, givenName, Ansel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ansel Context triple: [Ansel Briggs, givenName, Ansel]
-
A.
Ansel
chosen
Ansel is a masculine given name most famously associated with American landscape photographer Ansel Adams.
-
B.
Ansel Briggs
Ansel Briggs was an American politician who became the first governor of the state of Iowa in the mid-19th century.
-
C.
Ansen
Ansen is a small village in the Dutch province of Drenthe, located within the municipality of De Wolden.
-
D.
Orson
Orson is a masculine given name most famously associated with the American filmmaker and actor Orson Welles.
-
E.
Asner
Asner is the surname of American actor and activist Ed Asner, best known for his role as Lou Grant on "The Mary Tyler Moore Show" and its spin-off.
- 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b52896bc819086ef95cc63e90daa |
completed | April 16, 2026, 10:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff998fa5588190b28efc2f342405aa |
completed | May 9, 2026, 8:31 p.m. |
Created at: April 10, 2026, 4:48 a.m.