Triple
T10251028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francis |
E240339
|
entity |
| Predicate | hasFeminineForm |
P1613
|
FINISHED |
| Object | Frances |
E12143
|
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: Frances | Statement: [Francis, hasFeminineForm, Frances]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frances Context triple: [Francis, hasFeminineForm, Frances]
-
A.
Frances
chosen
Frances is a feminine given name of Latin origin, commonly used in English-speaking countries.
-
B.
Frances
Frances is the Allied reporting name for the Japanese Yokosuka P1Y twin-engine land-based bomber used by the Imperial Japanese Navy during World War II.
-
C.
Oneida
Oneida is an experimental rock band from Brooklyn, New York, known for its long-form, improvisational, and genre-blending psychedelic sound.
-
D.
Frederica
Frederica is a feminine given name of Germanic origin that has been borne by various European royals and notable women.
-
E.
Landes
Landes is a department in southwestern France known for its vast Atlantic coastline, extensive pine forests, and popular surfing beaches.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23d7300819095971560759cf456 |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71cc82110819095fb77eb964d23bd |
completed | April 9, 2026, 3:28 a.m. |
Created at: April 6, 2026, 11:28 a.m.