Triple
T14578672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nannette Streicher |
E342126
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Nannette |
E137909
|
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: Nannette | Statement: [Nannette Streicher, givenName, Nannette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nannette Context triple: [Nannette Streicher, givenName, Nannette]
-
A.
Nannie
chosen
Nannie is a feminine given name, often used as a diminutive or variant of names like Nancy or Anne.
-
B.
Claudette
Claudette is the given name of Claudette Colvin, a pioneering African American civil rights activist who challenged bus segregation in Montgomery, Alabama, prior to Rosa Parks.
-
C.
Nancy
Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
-
D.
Nancy
Nancy is a historic city in northeastern France renowned for its elegant 18th-century architecture and UNESCO-listed Place Stanislas.
-
E.
Lucille
"Lucille" is a 1977 country song by Kenny Rogers that became one of his signature hits and a classic of the genre.
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb3f6f78c81908a30ecb4c025299d |
completed | April 14, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8ad03e7881908a783182c6d656b5 |
completed | May 8, 2026, 7:03 a.m. |
Created at: April 10, 2026, 1:24 a.m.