Triple
T13359601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deutz AG |
E318785
|
entity |
| Predicate | hasDivision |
P35
|
FINISHED |
| Object |
Green Segment
Green Segment is a business division of Deutz AG focused on sustainable, low-emission and alternative drive technologies.
|
E1036664
|
NE FINISHED |
How this triple was built (4 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: Green Segment | Statement: [Deutz AG, hasDivision, Green Segment]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green Segment Context triple: [Deutz AG, hasDivision, Green Segment]
-
A.
Green
Green is a color commonly used in transit systems to designate specific routes or lines, such as the Green Line E branch streetcar.
-
B.
Green
Green is a common English surname of Anglo-Saxon origin, typically derived from a descriptive nickname related to the color green or someone who lived near a village green.
-
C.
Green
Green is a song featured on the album "Picture Perfect Morning."
-
D.
Blue Segment
Blue Segment is an abstract painting by Russian artist Wassily Kandinsky, exemplifying his pioneering exploration of color, form, and non-representational composition.
-
E.
Green line
The Green line is one of the main color-coded routes in the Stockholm metro system, serving numerous central and suburban stations across the city.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Green Segment Triple: [Deutz AG, hasDivision, Green Segment]
Generated description
Green Segment is a business division of Deutz AG focused on sustainable, low-emission and alternative drive technologies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Green Segment Target entity description: Green Segment is a business division of Deutz AG focused on sustainable, low-emission and alternative drive technologies.
-
A.
Green
Green is a color commonly used in transit systems to designate specific routes or lines, such as the Green Line E branch streetcar.
-
B.
Green
Green is a song featured on the album "Picture Perfect Morning."
-
C.
Green
Green is a common English surname of Anglo-Saxon origin, typically derived from a descriptive nickname related to the color green or someone who lived near a village green.
-
D.
Blue Segment
Blue Segment is an abstract painting by Russian artist Wassily Kandinsky, exemplifying his pioneering exploration of color, form, and non-representational composition.
-
E.
Green line
The Green line is one of the main color-coded routes in the Stockholm metro system, serving numerous central and suburban stations across the city.
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69da6289edf4819099b21cfbb668e923 |
completed | April 11, 2026, 3:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7267ab580819091577c24dd952c99 |
completed | May 3, 2026, 10:42 a.m. |
| NEDg | Description generation | batch_69f7277a73248190aa59a997d719cab8 |
completed | May 3, 2026, 10:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7281e150081909a92201ceb30b8d6 |
completed | May 3, 2026, 10:49 a.m. |
Created at: April 9, 2026, 9:32 p.m.