Triple
T19805182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strasbourg University Hospitals |
E475791
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object |
HUS
HUS is the commonly used abbreviation for the Strasbourg University Hospitals, a major French academic medical center and teaching hospital complex.
|
E1395684
|
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: HUS | Statement: [Strasbourg University Hospitals, hasAbbreviation, HUS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HUS Context triple: [Strasbourg University Hospitals, hasAbbreviation, HUS]
-
A.
HUS
HUS is the commonly used abbreviation for the Human Sciences program or faculty at Osaka University in Japan.
-
B.
HIB
HIB is the FAA airport code for Range Regional Airport, a public airport serving the Hibbing, Minnesota area.
-
C.
HIB
HIB is the National Rail station code for High Brooms railway station in Kent, England.
-
D.
HSP
HSP (Headset Profile) is a Bluetooth standard that defines how wireless headsets communicate with devices like phones and computers for basic audio and control functions.
-
E.
Hipple
Hipple is the birth surname of American film and television actor Hugh Marlowe.
- 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: HUS Triple: [Strasbourg University Hospitals, hasAbbreviation, HUS]
Generated description
HUS is the commonly used abbreviation for the Strasbourg University Hospitals, a major French academic medical center and teaching hospital complex.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HUS Target entity description: HUS is the commonly used abbreviation for the Strasbourg University Hospitals, a major French academic medical center and teaching hospital complex.
-
A.
HUS
HUS is the commonly used abbreviation for the Human Sciences program or faculty at Osaka University in Japan.
-
B.
HIB
HIB is the FAA airport code for Range Regional Airport, a public airport serving the Hibbing, Minnesota area.
-
C.
HIB
HIB is the National Rail station code for High Brooms railway station in Kent, England.
-
D.
HSP
HSP (Headset Profile) is a Bluetooth standard that defines how wireless headsets communicate with devices like phones and computers for basic audio and control functions.
-
E.
Hipple
Hipple is the birth surname of American film and television actor Hugh Marlowe.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65427546c819082c8eb0d63e3f5fe |
completed | April 20, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07c50c6a7c81908bf0d55dad7a6556 |
completed | May 16, 2026, 1:14 a.m. |
| NEDg | Description generation | batch_6a07c66178108190a6fc2d7566af44ad |
completed | May 16, 2026, 1:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07c73b731881909c8abaeb541e34f9 |
completed | May 16, 2026, 1:24 a.m. |
Created at: April 10, 2026, 1:49 p.m.