Triple
T15893709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helen Shaver |
E385395
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Shaver |
E314596
|
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: Shaver | Statement: [Helen Shaver, familyName, Shaver]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shaver Context triple: [Helen Shaver, familyName, Shaver]
-
A.
Shaver
chosen
Shaver is the middle name of James Shaver Woodsworth, a prominent Canadian social reformer and founding leader of the Co-operative Commonwealth Federation.
-
B.
Braun
Braun is a German surname most infamously associated with Eva Braun, the longtime companion and brief wife of Adolf Hitler.
-
C.
Cleaver
Cleaver is a surname most notably associated with Eldridge Cleaver, a prominent writer and former leader in the Black Panther Party.
-
D.
Gilette
Gilette is a small French commune in the Alpes-Maritimes department of southeastern France, known for its hilltop setting overlooking the confluence of the Var and Estéron rivers.
-
E.
Barbier
Barbier is a French surname and term historically associated with the profession of barbering.
- 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_69d86da5b800819083a31be937d738b0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1563727cc819086b5c18b655dd7f6 |
completed | April 16, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb0497cb481908e8ea4ebb9c4039d |
completed | May 9, 2026, 10:08 p.m. |
Created at: April 10, 2026, 4:51 a.m.