Triple
T15726985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS-Standarten |
E381241
|
entity |
| Predicate | subordinateTo |
P258
|
FINISHED |
| Object |
SS-Abschnitt
SS-Abschnitt was a mid-level regional command unit of the Nazi Schutzstaffel (SS) overseeing several subordinate SS-Standarten within a defined geographic area.
|
E1173348
|
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: SS-Abschnitt | Statement: [SS-Standarten, subordinateTo, SS-Abschnitt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SS-Abschnitt Context triple: [SS-Standarten, subordinateTo, SS-Abschnitt]
-
A.
.ss
.ss is the country code top-level domain (ccTLD) assigned to South Sudan for use in its internet addresses.
-
B.
SSA
SSA is a professional scientific organization dedicated to advancing the study and understanding of earthquakes and seismic phenomena.
-
C.
SSA
SSA is the commonly used acronym for Mexico’s federal Secretariat of Health, the government ministry responsible for national public health policy and services.
-
D.
SSA
SSA is the French Armed Forces Health Service, responsible for providing medical support and healthcare to military personnel in France and during overseas operations.
-
E.
SSA
SSA is a major multi-purpose indoor arena in Saitama, Japan, known for hosting large-scale sports events, concerts, and entertainment shows.
- 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: SS-Abschnitt Triple: [SS-Standarten, subordinateTo, SS-Abschnitt]
Generated description
SS-Abschnitt was a mid-level regional command unit of the Nazi Schutzstaffel (SS) overseeing several subordinate SS-Standarten within a defined geographic area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SS-Abschnitt Target entity description: SS-Abschnitt was a mid-level regional command unit of the Nazi Schutzstaffel (SS) overseeing several subordinate SS-Standarten within a defined geographic area.
-
A.
.ss
.ss is the country code top-level domain (ccTLD) assigned to South Sudan for use in its internet addresses.
-
B.
SSA
SSA is a professional scientific organization dedicated to advancing the study and understanding of earthquakes and seismic phenomena.
-
C.
SSA
SSA is the commonly used acronym for Mexico’s federal Secretariat of Health, the government ministry responsible for national public health policy and services.
-
D.
SSA
SSA is the French Armed Forces Health Service, responsible for providing medical support and healthcare to military personnel in France and during overseas operations.
-
E.
SSA
SSA is a major multi-purpose indoor arena in Saitama, Japan, known for hosting large-scale sports events, concerts, and entertainment shows.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb357a88190a92641c8a8c20573 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82faa5508190a28e2a224d4a4a06 |
completed | May 9, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_69ff8388b3588190ae55c123bb19cb2c |
completed | May 9, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff84125e808190a4d465d9effad639 |
completed | May 9, 2026, 6:59 p.m. |
Created at: April 10, 2026, 4:46 a.m.