Triple
T6434825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonin Scalia |
E129866
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Antonin |
E129866
|
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: Antonin | Statement: [Antonin Scalia, givenName, Antonin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antonin Context triple: [Antonin Scalia, givenName, Antonin]
-
A.
Antonin
chosen
Antonin is a masculine given name most notably borne by Antonin Scalia, a former Associate Justice of the United States Supreme Court.
-
B.
Antoine
Antoine is the given name of Antoine de la Mothe Cadillac, the French explorer and founder of Detroit.
-
C.
René
René is a French given name commonly used for males and historically associated with several notable figures in politics, arts, and philosophy.
-
D.
Théodore
Théodore is a masculine given name of Greek origin, commonly used in French-speaking countries and borne by notable figures such as the Reformation theologian Théodore Beza.
-
E.
Anatole
Anatole is the famously temperamental and gifted French chef employed by Aunt Dahlia in P. G. Wodehouse’s Jeeves and Wooster stories.
- 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_69c0084caac48190a7bc2ad8ba44536f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c069415c3c8190b91bd12ae79edd26 |
completed | March 22, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c640efd490819084b3b67b3b0680b6 |
completed | March 27, 2026, 8:33 a.m. |
Created at: March 22, 2026, 4:45 p.m.