Triple
T20314586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ICE T |
E510345
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object |
DWA
DWA is a company known for manufacturing the ICE T high-speed tilting trains used in Germany’s rail network.
|
E1424295
|
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: DWA | Statement: [ICE T, manufacturer, DWA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DWA Context triple: [ICE T, manufacturer, DWA]
-
A.
DWA
DWA is the commonly used abbreviation for the International Labour Organization’s Decent Work Agenda, a global framework promoting fair, secure, and dignified employment.
-
B.
DWD
DWD is the three-letter National Rail station code for Dolwyddelan railway station in Wales.
-
C.
DWB
DWB is the abbreviation for the Deutscher Werkbund, a pioneering German association of artists, architects, designers, and industrialists founded in 1907 that significantly influenced modern design and architecture.
-
D.
WADW
WADW is the ICAO airport code for Umbu Mehang Kunda Airport in Indonesia.
-
E.
DW
DW is the commonly used abbreviation for Daniel Wellington, a Swedish watch and accessories brand known for its minimalist, classic designs.
- 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: DWA Triple: [ICE T, manufacturer, DWA]
Generated description
DWA is a company known for manufacturing the ICE T high-speed tilting trains used in Germany’s rail network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DWA Target entity description: DWA is a company known for manufacturing the ICE T high-speed tilting trains used in Germany’s rail network.
-
A.
DWA
DWA is the commonly used abbreviation for the International Labour Organization’s Decent Work Agenda, a global framework promoting fair, secure, and dignified employment.
-
B.
DWD
DWD is the three-letter National Rail station code for Dolwyddelan railway station in Wales.
-
C.
DWB
DWB is the abbreviation for the Deutscher Werkbund, a pioneering German association of artists, architects, designers, and industrialists founded in 1907 that significantly influenced modern design and architecture.
-
D.
WADW
WADW is the ICAO airport code for Umbu Mehang Kunda Airport in Indonesia.
-
E.
DW
DW is the commonly used abbreviation for Daniel Wellington, a Swedish watch and accessories brand known for its minimalist, classic designs.
- 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_69e0b4c7491c8190961113c4283b10b0 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e67786f4dc8190b02a6c2a4338362d |
completed | April 20, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08612293088190a06e4d22cca8a285 |
completed | May 16, 2026, 12:20 p.m. |
| NEDg | Description generation | batch_6a086255182881908eeaa49d34bf3ba5 |
completed | May 16, 2026, 12:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08635869148190992d75a8806f767c |
completed | May 16, 2026, 12:30 p.m. |
Created at: April 16, 2026, 11:19 a.m.