Triple
T18386350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hochschule Darmstadt University of Applied Sciences |
E446602
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
h_da
h_da is the commonly used abbreviation for Hochschule Darmstadt, a major German University of Applied Sciences known for its practice-oriented programs and research.
|
E1320977
|
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: h_da | Statement: [Hochschule Darmstadt University of Applied Sciences, shortName, h_da]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: h_da Context triple: [Hochschule Darmstadt University of Applied Sciences, shortName, h_da]
-
A.
HDA
HDA is the ICAO airline designator assigned to Cathay Dragon, the former Hong Kong-based regional carrier of the Cathay Pacific Group.
-
B.
Ha
"Ha" is a track by rapper Juvenile, notable for its distinctive second-person narrative style and repetitive use of the word "ha," from his influential 1998 album *400 Degreez*.
-
C.
HAJ
HAJ is the three-letter IATA airport code for Hannover Airport in Hanover, Germany.
-
D.
HAD
HAD is the commonly used abbreviation for the Historical Astronomy Division, a group focused on the study and promotion of the history of astronomy.
-
E.
HAD
HAD is the station code used to identify Hallunda metro station in the Stockholm Metro system.
- 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: h_da Triple: [Hochschule Darmstadt University of Applied Sciences, shortName, h_da]
Generated description
h_da is the commonly used abbreviation for Hochschule Darmstadt, a major German University of Applied Sciences known for its practice-oriented programs and research.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: h_da Target entity description: h_da is the commonly used abbreviation for Hochschule Darmstadt, a major German University of Applied Sciences known for its practice-oriented programs and research.
-
A.
HDA
HDA is the ICAO airline designator assigned to Cathay Dragon, the former Hong Kong-based regional carrier of the Cathay Pacific Group.
-
B.
Ha
"Ha" is a track by rapper Juvenile, notable for its distinctive second-person narrative style and repetitive use of the word "ha," from his influential 1998 album *400 Degreez*.
-
C.
HAJ
HAJ is the three-letter IATA airport code for Hannover Airport in Hanover, Germany.
-
D.
HAD
HAD is the commonly used abbreviation for the Historical Astronomy Division, a group focused on the study and promotion of the history of astronomy.
-
E.
HAD
HAD is the station code used to identify Hallunda metro station in the Stockholm Metro system.
- 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e517a00c608190a8f0010c7b53df90 |
completed | April 19, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03d784b3008190b84905ed433d846d |
completed | May 13, 2026, 1:44 a.m. |
| NEDg | Description generation | batch_6a03d8512d4c8190b113f7ed1f8a044c |
completed | May 13, 2026, 1:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03d8d863cc819098b46d5d26db05cd |
completed | May 13, 2026, 1:50 a.m. |
Created at: April 10, 2026, 10:46 a.m.