Triple
T9596940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alfonse D'Amato |
E231553
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object |
Christopher D'Amato
Christopher D'Amato is known primarily as the son of former U.S. Senator Alfonse D'Amato.
|
E808873
|
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: Christopher D'Amato | Statement: [Alfonse D'Amato, child, Christopher D'Amato]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christopher D'Amato Context triple: [Alfonse D'Amato, child, Christopher D'Amato]
-
A.
Eusebio Bava
Eusebio Bava was a 19th-century Italian general and statesman who played a key role in the military campaigns leading up to the unification of Italy.
-
B.
Dario Argento
Dario Argento is an Italian filmmaker renowned as a master of giallo and horror cinema, known for his visually stylized, psychologically intense thrillers.
-
C.
Nicolas de Toth
Nicolas de Toth is a film editor best known for his work on major Hollywood action movies, including Terminator 3: Rise of the Machines.
-
D.
Jean Mascolo
Jean Mascolo is the son of renowned French writer and filmmaker Marguerite Duras and has worked as a filmmaker and documentarian.
-
E.
Jorge Bava
Jorge Bava is a Uruguayan former professional goalkeeper who became a football manager, known for coaching clubs in Latin American leagues.
- 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: Christopher D'Amato Triple: [Alfonse D'Amato, child, Christopher D'Amato]
Generated description
Christopher D'Amato is known primarily as the son of former U.S. Senator Alfonse D'Amato.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Christopher D'Amato Target entity description: Christopher D'Amato is known primarily as the son of former U.S. Senator Alfonse D'Amato.
-
A.
Eusebio Bava
Eusebio Bava was a 19th-century Italian general and statesman who played a key role in the military campaigns leading up to the unification of Italy.
-
B.
Dario Argento
Dario Argento is an Italian filmmaker renowned as a master of giallo and horror cinema, known for his visually stylized, psychologically intense thrillers.
-
C.
Nicolas de Toth
Nicolas de Toth is a film editor best known for his work on major Hollywood action movies, including Terminator 3: Rise of the Machines.
-
D.
Jean Mascolo
Jean Mascolo is the son of renowned French writer and filmmaker Marguerite Duras and has worked as a filmmaker and documentarian.
-
E.
Jorge Bava
Jorge Bava is a Uruguayan former professional goalkeeper who became a football manager, known for coaching clubs in Latin American leagues.
- 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_69ca8482884481908eccdfdf64d6fbf7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a34f5408190ba72ba8311eb259c |
completed | April 1, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1619f4170819092ae90b2896b0855 |
completed | April 4, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69d163c1abfc8190baae07681ea13104 |
completed | April 4, 2026, 7:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d16476b9188190ab28efb0433c99b0 |
completed | April 4, 2026, 7:20 p.m. |
Created at: March 30, 2026, 8:07 p.m.