Triple
T12104814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nancy (With the Laughing Face) |
E288274
|
entity |
| Predicate | recordedBy |
P1165
|
FINISHED |
| Object | Jack Jones |
E518947
|
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: Jack Jones | Statement: [Nancy (With the Laughing Face), recordedBy, Jack Jones]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jack Jones Context triple: [Nancy (With the Laughing Face), recordedBy, Jack Jones]
-
A.
Jack Jones
chosen
Jack Jones is an American traditional pop and jazz singer known for his smooth baritone voice and classic interpretations of standards.
-
B.
Jack Jones
Jack Jones was a prominent British trade union leader and influential figure in the labor movement during the mid-20th century.
-
C.
Mel Jones
Mel Jones is an animated character best known as the mother of the protagonist Coraline in the 2009 stop-motion film "Coraline."
-
D.
Tom Jones
Tom Jones is a 1963 British comedy-adventure film, based on Henry Fielding’s novel, that became a critical and commercial success and won the Academy Award for Best Picture.
-
E.
Tom Jones
Tom Jones is the charismatic, high-spirited protagonist of Henry Fielding’s classic 18th-century comic novel, known for his romantic escapades and moral growth.
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91561eaec819096ba00682d81f41a |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f677039481908f14fa12b9b86910 |
completed | May 2, 2026, 1:04 p.m. |
Created at: April 8, 2026, 9:48 p.m.