Triple
T7694202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | the Hotel Landlady |
E174329
|
entity |
| Predicate | appearsAlongsideCharacter |
P25756
|
FINISHED |
| Object | Miss Prym |
E682773
|
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: Miss Prym | Statement: [the Hotel Landlady, appearsAlongsideCharacter, Miss Prym]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miss Prym Context triple: [the Hotel Landlady, appearsAlongsideCharacter, Miss Prym]
-
A.
Miss Prym
chosen
Miss Prym is the central protagonist of Paulo Coelho’s novel "The Devil and Miss Prym," a young woman in a small village who becomes embroiled in a moral dilemma when confronted with a stranger’s dark proposal.
-
B.
Prissy
Prissy is a diminutive nickname for the given name Priscilla, often used as an affectionate or informal form.
-
C.
Mathilda
Mathilda is the middle name of Elivera Mathilda Carlson Doud, the wife of former U.S. President Dwight D. Eisenhower.
-
D.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
-
E.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
- 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_69c6995966348190939e6c37ba272c06 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702459f988190bf7087bf51d5317f |
completed | March 27, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b4fda81881908144cebdd2696e63 |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:02 p.m.