Triple
T1238247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hannah |
E26596
|
entity |
| Predicate | cognateOf |
P8954
|
FINISHED |
| Object | Anne |
E174934
|
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: Anne | Statement: [Hannah, cognateOf, Anne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anne Context triple: [Hannah, cognateOf, Anne]
-
A.
Anne
chosen
Anne is traditionally revered in Christian tradition as the mother of the Virgin Mary and the grandmother of Jesus.
-
B.
Anne
Anne is the given name of Anne Morrow Lindbergh, the American author and aviator who was married to famed aviator Charles Lindbergh.
-
C.
Kate
Kate is a common diminutive form of the given name Catherine, frequently used in English-speaking countries.
-
D.
Kate
Kate is the Allied reporting name for the Nakajima B5N, a Japanese World War II carrier-based torpedo bomber aircraft.
-
E.
Elisabeth
Elisabeth is a feminine given name of Hebrew origin, commonly used in various European languages as a form of Elizabeth.
- 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_69a4948571c88190a9191e451e6035fd |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf406b988190a12aa26bbcb88d6a |
completed | March 1, 2026, 10:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad51996dc08190afecb40095e3d3e2 |
completed | March 8, 2026, 10:38 a.m. |
Created at: March 1, 2026, 7:47 p.m.