Triple
T9068933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony |
E217312
|
entity |
| Predicate | loveInterestOf |
P7325
|
FINISHED |
| Object | Maria |
unclear NED1
|
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: Maria | Statement: [Tony, loveInterestOf, Maria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maria Context triple: [Tony, loveInterestOf, Maria]
-
A.
Maria
Maria is an Italian woman best known as the younger sister of actress Sophia Loren and the former wife of film producer Romano Mussolini.
-
B.
Maria
Maria is a character in the period drama film "Stage Beauty," which explores gender roles and the world of 17th-century English theatre.
-
C.
Maria
Maria I of Portugal was the first queen regnant of Portugal, known for her devout Catholicism, initial period of enlightened reforms, and later mental illness that led to her son acting as regent.
-
D.
Maria
Maria is a witty and sharp-tongued lady-in-waiting to the Princess of France in William Shakespeare’s comedy "Love's Labour's Lost."
-
E.
Maria
Maria is a coastal municipality on Siquijor Island in the Philippines known for its rural communities and scenic seaside landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69ca83d5a7f48190b16c1e59bd43ede0 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc955ba250819085fa49e0059d06c1 |
completed | April 1, 2026, 3:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d017a3926881909140f59c60ec3588 |
completed | April 3, 2026, 7:40 p.m. |
Created at: March 30, 2026, 7:11 p.m.