Triple
T12761970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flix SE |
E305016
|
entity |
| Predicate | acquisitionTargetIndustry |
P106769
|
FINISHED |
| Object | intercity bus transport in North America |
—
|
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: intercity bus transport in North America | Statement: [Flix SE, acquisitionTargetIndustry, intercity bus transport in North America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: acquisitionTargetIndustry Context triple: [Flix SE, acquisitionTargetIndustry, intercity bus transport in North America]
-
A.
acquisitionTarget
Indicates that one entity is the intended or actual company or asset being acquired by another in a merger or acquisition transaction.
-
B.
acquisitionCategory
Indicates the type or classification of an acquisition associated with an entity or transaction.
-
C.
targetIndustryDepicted
Indicates that an entity visually represents or portrays a specific industry as its primary subject or focus.
-
D.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
E.
targetsSector
Indicates that an entity is directed toward, focused on, or intended to affect a particular economic or industry sector.
- 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d8e44188190840cd23d380bf23d |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96409739881909174ba005a986cb5 |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d87078c819083ea724238992204 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:28 p.m.