Triple
T22116514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emily Hobhouse |
E546557
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Emily |
—
|
NE NERFINISHED |
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: Emily | Statement: [Emily Hobhouse, givenName, Emily]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emily Context triple: [Emily Hobhouse, givenName, Emily]
-
A.
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.
-
B.
Emily
chosen
Emily is a given name commonly used in English-speaking countries, often associated with literary, historical, and contemporary cultural figures.
-
C.
Emily
Emily is the NATO reporting name for the Kawanishi H8K, a World War II-era Japanese four-engine flying boat used primarily for long-range maritime patrol and reconnaissance.
-
D.
Emily
Emily is the tragic, ghostly bride from Tim Burton’s animated film "Corpse Bride," who falls in love with Victor Van Dort in the Land of the Dead.
-
E.
Emily
Emily is a fictional character from the romantic comedy film "The Perfect Holiday," which centers on festive romance and family dynamics during the Christmas season.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e38b3848190ac3a4fa97d56e65a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1294dc1148190b95ff00f475a6713 |
completed | April 28, 2026, 9:40 p.m. |
Created at: April 16, 2026, 8:31 p.m.