Triple
T14935735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bob Lilly |
E372385
|
entity |
| Predicate | occupationAfterFootball |
P75487
|
FINISHED |
| Object | photographer |
—
|
LITERAL 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: photographer | Statement: [Bob Lilly, occupationAfterFootball, photographer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationAfterFootball Context triple: [Bob Lilly, occupationAfterFootball, photographer]
-
A.
hasProfessionOutsideFootball
Indicates that a person involved in football also holds a separate professional occupation outside of football.
-
B.
careerEndLeague
Indicates the league in which an entity’s professional career officially ended.
-
C.
returnedToFootball
Indicates that an entity resumed participating in football after having previously stopped or taken a break.
-
D.
tookBreakFromFootball
Indicates that an entity temporarily stopped participating in football for a period of time.
-
E.
memberLaterOccupation
chosen
Indicates that an individual later held a particular occupation or position after an earlier point in time or role.
- F. None of above.
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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded647ae388190a0e97c03f2a4d832 |
completed | April 15, 2026, 12:05 a.m. |
| PD | Predicate disambiguation | batch_69de9a52ba988190a26e268b4ea083ea |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:37 a.m.