Triple
T12772139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Cenerentola |
E305270
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Dandini
Dandini is the valet in Rossini’s opera "La Cenerentola" who swaps roles with his master, the prince, to comic effect.
|
E1002987
|
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: Dandini | Statement: [La Cenerentola, character, Dandini]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dandini Context triple: [La Cenerentola, character, Dandini]
-
A.
Daggubati
Daggubati is an Indian family name notably associated with a prominent film-producing and acting dynasty in the Telugu cinema industry.
-
B.
Dashanana
Dashanana is an epithet of the demon-king Ravana from the Hindu epic Ramayana, highlighting his legendary form with ten heads and immense power.
-
C.
Ranchipur
Ranchipur is the fictional Indian princely city that serves as the central backdrop for Louis Bromfield’s novel and its film adaptation "The Rains Came."
-
D.
Chakari
Chakari is a small mining and agricultural town located in the Mashonaland West Province of Zimbabwe.
-
E.
Nandha
Nandha is a 2001 Tamil-language drama film directed by Bala, widely recognized for Suriya’s breakthrough performance in a gritty, emotionally intense role.
- 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: Dandini Triple: [La Cenerentola, character, Dandini]
Generated description
Dandini is the valet in Rossini’s opera "La Cenerentola" who swaps roles with his master, the prince, to comic effect.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dandini Target entity description: Dandini is the valet in Rossini’s opera "La Cenerentola" who swaps roles with his master, the prince, to comic effect.
-
A.
Daggubati
Daggubati is an Indian family name notably associated with a prominent film-producing and acting dynasty in the Telugu cinema industry.
-
B.
Dashanana
Dashanana is an epithet of the demon-king Ravana from the Hindu epic Ramayana, highlighting his legendary form with ten heads and immense power.
-
C.
Ranchipur
Ranchipur is the fictional Indian princely city that serves as the central backdrop for Louis Bromfield’s novel and its film adaptation "The Rains Came."
-
D.
Chakari
Chakari is a small mining and agricultural town located in the Mashonaland West Province of Zimbabwe.
-
E.
Nandha
Nandha is a 2001 Tamil-language drama film directed by Bala, widely recognized for Suriya’s breakthrough performance in a gritty, emotionally intense role.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96df5b68481908a5d40516b09be52 |
completed | April 10, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f684fcd4b48190ab610efffcbd1546 |
completed | May 2, 2026, 11:13 p.m. |
| NEDg | Description generation | batch_69f689e108cc819097bacb28f6bd9a9b |
completed | May 2, 2026, 11:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f68adcfabc8190a85624e2f1b0bafb |
completed | May 2, 2026, 11:38 p.m. |
Created at: April 9, 2026, 5:28 p.m.