Triple
T27893624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Divock Origi |
E705425
|
entity |
| Predicate | jerseyNumberAtLiverpool |
P164360
|
FINISHED |
| Object | 27 |
—
|
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: 27 | Statement: [Divock Origi, jerseyNumberAtLiverpool, 27]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jerseyNumberAtLiverpool Context triple: [Divock Origi, jerseyNumberAtLiverpool, 27]
-
A.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
B.
hasJerseyNumberRetired
Indicates that an entity has had its jersey number officially retired, typically in recognition of its contributions or achievements.
-
C.
liverpoolCaptain
Indicates that one entity serves as the captain of the Liverpool football team at a given time in relation to another entity.
-
D.
clubNumberAtManchesterUnited
Indicates the squad number a player wears or wore while playing for Manchester United.
-
E.
jerseyNumberTeam
Indicates the association between a specific jersey number and the team for which that jersey number is used or assigned.
- F. None of above. chosen
Provenance (4 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_69ef96b490ac8190a412d04c5d009f3e |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f644de4a84819087ddb84757fc4585 |
completed | May 2, 2026, 6:39 p.m. |
| PD | Predicate disambiguation | batch_69f641def1e88190a05bf865ced78b23 |
completed | May 2, 2026, 6:26 p.m. |
| PDg | Predicate description generation | batch_69f643e818d481908fc66bc91bd25d77 |
completed | May 2, 2026, 6:35 p.m. |
Created at: April 27, 2026, 6:37 p.m.