Triple
T36123642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Citroën C4 WRC |
E1044814
|
entity |
| Predicate | primaryLiverySponsor |
P204751
|
FINISHED |
| Object | Total |
E555210
|
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: Total | Statement: [Citroën C4 WRC, primaryLiverySponsor, Total]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryLiverySponsor Context triple: [Citroën C4 WRC, primaryLiverySponsor, Total]
-
A.
liveryType
Indicates the specific style, design, or branding scheme applied to an entity’s exterior appearance.
-
B.
liveryInspiredBy
Indicates that one livery’s design, colors, or overall appearance is based on, influenced by, or pays homage to another livery.
-
C.
liveryFeature
Indicates a characteristic or design element that is part of a specific livery or external appearance scheme.
-
D.
liveryNumber
Indicates the identifying number assigned to a vehicle or asset within a particular livery or fleet.
-
E.
liveryColors
Indicates the specific set of colors used as the official or characteristic color scheme associated with an entity (such as a brand, organization, or vehicle).
- F. None of above. chosen
Provenance (5 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_69f76e356c908190abc6ca1e6a05b011 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a38de0ed2f88190a8727d43bbfa9c35 |
completed | June 22, 2026, 7:02 a.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:08 p.m.