Triple
T13309749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johnny Eager |
E317028
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Robert Taylor |
E226052
|
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: Robert Taylor | Statement: [Johnny Eager, starring, Robert Taylor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert Taylor Context triple: [Johnny Eager, starring, Robert Taylor]
-
A.
Robert Taylor
Robert Taylor was an American civic leader and housing advocate in Chicago, best known for his work on public housing and for whom the Robert Taylor Homes were named.
-
B.
Robert Taylor
chosen
Robert Taylor was a prominent American film and television actor of Hollywood’s Golden Age, known for his leading-man roles in classics such as "Camille," "Waterloo Bridge," and "Quo Vadis."
-
C.
Robert Taylor
Robert Taylor was the husband of legendary Chicago blues singer Koko Taylor, known primarily in relation to her life and career.
-
D.
Robert Taylor
Robert Taylor was an influential American computer scientist and research manager who played a key role in the development of ARPANET and the foundations of the modern internet.
-
E.
Ray Taylor
Ray Taylor was an American film director best known for his work on action-packed serials and B-movies during the early 20th century.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990f56abc8190951774a999e2ce11 |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716e58cc48190afb46e8394227ab5 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:29 p.m.