Triple
T11443162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emilia Galotti |
E271195
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Marinelli
Marinelli is a manipulative courtier and the prince’s scheming advisor in Gotthold Ephraim Lessing’s tragedy "Emilia Galotti."
|
E926235
|
NE FINISHED |
How this triple was built (4 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: Marinelli | Statement: [Emilia Galotti, mainCharacter, Marinelli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marinelli Context triple: [Emilia Galotti, mainCharacter, Marinelli]
-
A.
Piermarini
Piermarini is an Italian surname most notably associated with Giuseppe Piermarini, an 18th-century architect renowned for designing Milan’s Teatro alla Scala.
-
B.
Marianelli
Marianelli is an Italian surname most notably associated with Academy Award–winning film composer Dario Marianelli.
-
C.
Mattioli
Mattioli is an Italian surname historically associated with figures such as the 17th-century statesman Ercole Antonio Mattioli.
-
D.
Tamburini
Tamburini is an Italian-origin surname borne by various notable figures, including architects, musicians, and designers.
-
E.
Morbidelli
Morbidelli was an Italian motorcycle manufacturer and racing team known for its success in Grand Prix motorcycle racing, particularly in the 1970s.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Marinelli Triple: [Emilia Galotti, mainCharacter, Marinelli]
Generated description
Marinelli is a manipulative courtier and the prince’s scheming advisor in Gotthold Ephraim Lessing’s tragedy "Emilia Galotti."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marinelli Target entity description: Marinelli is a manipulative courtier and the prince’s scheming advisor in Gotthold Ephraim Lessing’s tragedy "Emilia Galotti."
-
A.
Piermarini
Piermarini is an Italian surname most notably associated with Giuseppe Piermarini, an 18th-century architect renowned for designing Milan’s Teatro alla Scala.
-
B.
Marianelli
Marianelli is an Italian surname most notably associated with Academy Award–winning film composer Dario Marianelli.
-
C.
Mattioli
Mattioli is an Italian surname historically associated with figures such as the 17th-century statesman Ercole Antonio Mattioli.
-
D.
Tamburini
Tamburini is an Italian-origin surname borne by various notable figures, including architects, musicians, and designers.
-
E.
Morbidelli
Morbidelli was an Italian motorcycle manufacturer and racing team known for its success in Grand Prix motorcycle racing, particularly in the 1970s.
- F. None of above. chosen
Provenance (5 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d808891b7481908bf5a80cb7644061 |
completed | April 9, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5d3a2a68481909704ef9a7f780afc |
completed | April 20, 2026, 7:20 a.m. |
| NEDg | Description generation | batch_69e5d6a10c0c8190a96a4fc4ef330e8b |
completed | April 20, 2026, 7:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5d7fd235081909870476cbc9817b2 |
completed | April 20, 2026, 7:38 a.m. |
Created at: April 8, 2026, 9:35 p.m.