Triple
T603769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henrietta Emma Darwin |
E11551
|
entity |
| Predicate | middleName |
P143
|
FINISHED |
| Object | Emma |
E30843
|
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: Emma | Statement: [Henrietta Emma Darwin, middleName, Emma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emma Context triple: [Henrietta Emma Darwin, middleName, Emma]
-
A.
Emma
chosen
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
-
B.
Emily
Emily Warren Roebling was a pioneering 19th-century American engineer best known for her crucial role in overseeing the completion of the Brooklyn Bridge.
-
C.
Anna
Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
-
D.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
-
E.
Anna
Anna is a central female character in the comedy Western film "A Million Ways to Die in the West," portrayed as a sharp-shooting, quick-witted woman who helps the protagonist toughen up in the dangerous frontier.
- 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_69a4932779b881908688590d59c71900 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49dc67b248190b0bb195553f03be8 |
completed | March 1, 2026, 8:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a56930dfd88190a991adafc406c5ac |
completed | March 2, 2026, 10:40 a.m. |
Created at: March 1, 2026, 7:35 p.m.