Triple
T2462501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ford Bronco II |
E54562
|
entity |
| Predicate | trimLevelExample |
P11486
|
FINISHED |
| Object | XL |
—
|
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: XL | Statement: [Ford Bronco II, trimLevelExample, XL]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trimLevelExample Context triple: [Ford Bronco II, trimLevelExample, XL]
-
A.
trimLevel
chosen
Indicates the specific configuration or package level of features or options applied to an item, typically distinguishing variants within the same base model.
-
B.
representedLevel
Indicates that one entity denotes or encodes the degree, intensity, or value (i.e., the level) of another entity or property.
-
C.
tarLevel
Indicates the degree or amount of tar associated with or produced by something in the relationship.
-
D.
coversLevel
Indicates that one entity includes or encompasses a particular level or layer of another entity or system.
-
E.
representationLevel
Indicates the degree or layer at which something stands in for, models, or symbolizes something else (e.g., more concrete vs. more abstract representation).
- 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_69ab49dee84c819096b50a0049c347ac |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd49c5aa081909ab4f726a458b77f |
completed | March 7, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69abd0b199488190aa381b36593ae1ac |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:44 p.m.