Triple
T2844603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Democratic Women’s League of Germany |
E62553
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
DFD
DFD is the abbreviation for the Democratic Women’s League of Germany, a mass women’s organization that operated in East Germany.
|
E304071
|
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: DFD | Statement: [Democratic Women’s League of Germany, shortName, DFD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DFD Context triple: [Democratic Women’s League of Germany, shortName, DFD]
-
A.
DFD
DFD is the commonly used abbreviation for the Division of Fluid Dynamics, a professional organization focused on the study and advancement of fluid mechanics research.
-
B.
SFD
SFD is the National Rail station code for Salford Central railway station in Greater Manchester, England.
-
C.
FDD
FDD (Frequency Division Duplex) is a communication method that separates uplink and downlink signals onto different frequency bands to enable simultaneous two-way wireless transmission.
-
D.
DDD
DDD is a graphical front-end interface for the GNU Debugger (GDB) that provides a visual environment for debugging programs.
-
E.
DF
DF is the vehicle registration code for Brazil’s Federal District, which includes the national capital, Brasília.
- 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: DFD Triple: [Democratic Women’s League of Germany, shortName, DFD]
Generated description
DFD is the abbreviation for the Democratic Women’s League of Germany, a mass women’s organization that operated in East Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DFD Target entity description: DFD is the abbreviation for the Democratic Women’s League of Germany, a mass women’s organization that operated in East Germany.
-
A.
DFD
DFD is the commonly used abbreviation for the Division of Fluid Dynamics, a professional organization focused on the study and advancement of fluid mechanics research.
-
B.
SFD
SFD is the National Rail station code for Salford Central railway station in Greater Manchester, England.
-
C.
FDD
FDD (Frequency Division Duplex) is a communication method that separates uplink and downlink signals onto different frequency bands to enable simultaneous two-way wireless transmission.
-
D.
DDD
DDD is a graphical front-end interface for the GNU Debugger (GDB) that provides a visual environment for debugging programs.
-
E.
DF
DF is the vehicle registration code for Brazil’s Federal District, which includes the national capital, Brasília.
- 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_69ab4c3d16bc81908b3a1c98fbd287fe |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf1b58c88190b45d8c5a76dc52ac |
completed | March 7, 2026, 8:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afe8d850b481909850ff5e89021824 |
completed | March 10, 2026, 9:48 a.m. |
| NEDg | Description generation | batch_69afe990ce088190b42b20037c1eef3f |
completed | March 10, 2026, 9:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b00d04f42081909d59e1ad1bec6c34 |
completed | March 10, 2026, 12:22 p.m. |
Created at: March 6, 2026, 10:02 p.m.