Triple
T12643410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Countess Emma Luana of Orange-Nassau |
E301956
|
entity |
| Predicate | givenName |
P17
|
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: [Countess Emma Luana of Orange-Nassau, givenName, Emma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emma Context triple: [Countess Emma Luana of Orange-Nassau, givenName, Emma]
-
A.
Emma
chosen
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
-
B.
Emma
"Emma" is a 2009 British television miniseries adaptation of Jane Austen's novel, starring Romola Garai in the title role.
-
C.
Emma
Emma is a central character in Sam Shepard’s play "Curse of the Starving Class," portrayed as a rebellious and sharp-witted teenage girl struggling against her dysfunctional family and bleak circumstances.
-
D.
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.
-
E.
Emily
Emily is a given name commonly used in English-speaking countries, often associated with literary, historical, and contemporary cultural figures.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9614bf2f881909976becdf747f4fb |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68eab6148819080a58e20499186fa |
completed | May 2, 2026, 11:54 p.m. |
Created at: April 9, 2026, 5:17 p.m.