Triple
T17177635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Women |
E416901
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Cesira
Cesira is the resilient Roman shopkeeper and mother at the heart of Alberto Moravia’s novel and Vittorio De Sica’s film “Two Women,” famously portrayed by Sophia Loren.
|
E1254244
|
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: Cesira | Statement: [Two Women, mainCharacter, Cesira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cesira Context triple: [Two Women, mainCharacter, Cesira]
-
A.
Serbelloni
Serbelloni is an Italian noble family name historically associated with aristocratic lineages and prominent figures in Italy.
-
B.
Rescigno
Rescigno is an Italian surname associated with figures such as conductor Nicola Rescigno, known for his contributions to opera and classical music.
-
C.
Scarlino
Scarlino is a historic Tuscan town in central Italy, known for its medieval hilltop setting overlooking the Tyrrhenian coast.
-
D.
Carrà
Carrà is an Italian surname most notably associated with Carlo Carrà, a leading painter of the Futurist movement.
-
E.
Badoer
Badoer is an Italian noble family name historically associated with Venetian patricians and figures such as Donata Badoer, the wife of Marco Polo.
- 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: Cesira Triple: [Two Women, mainCharacter, Cesira]
Generated description
Cesira is the resilient Roman shopkeeper and mother at the heart of Alberto Moravia’s novel and Vittorio De Sica’s film “Two Women,” famously portrayed by Sophia Loren.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cesira Target entity description: Cesira is the resilient Roman shopkeeper and mother at the heart of Alberto Moravia’s novel and Vittorio De Sica’s film “Two Women,” famously portrayed by Sophia Loren.
-
A.
Serbelloni
Serbelloni is an Italian noble family name historically associated with aristocratic lineages and prominent figures in Italy.
-
B.
Rescigno
Rescigno is an Italian surname associated with figures such as conductor Nicola Rescigno, known for his contributions to opera and classical music.
-
C.
Scarlino
Scarlino is a historic Tuscan town in central Italy, known for its medieval hilltop setting overlooking the Tyrrhenian coast.
-
D.
Carrà
Carrà is an Italian surname most notably associated with Carlo Carrà, a leading painter of the Futurist movement.
-
E.
Badoer
Badoer is an Italian noble family name historically associated with Venetian patricians and figures such as Donata Badoer, the wife of Marco Polo.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc0ee5008190a73875b39841fd9f |
completed | April 18, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0148455afc8190931ba1316705ae0c |
completed | May 11, 2026, 3:08 a.m. |
| NEDg | Description generation | batch_6a014a2f8fec8190b1303967a76ceb63 |
completed | May 11, 2026, 3:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a014ab0be388190a6ce49cff469fe81 |
completed | May 11, 2026, 3:19 a.m. |
Created at: April 10, 2026, 5:37 a.m.