Triple
T9540639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regen (district) |
E230146
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
REG
REG is the vehicle registration code assigned to the Regen district in Germany.
|
E805655
|
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: REG | Statement: [Regen (district), vehicleRegistrationCode, REG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: REG Context triple: [Regen (district), vehicleRegistrationCode, REG]
-
A.
Reg
Reg is a common shortened given name, typically derived from Reginald.
-
B.
Registry
The Registry is the administrative organ of the International Criminal Tribunal for Rwanda responsible for managing court services, legal support, and overall tribunal operations.
-
C.
Reg D
Reg D is a Federal Reserve regulation that governs reserve requirements and certain aspects of deposit accounts and transaction limitations for U.S. depository institutions.
-
D.
Reg A
Reg A is a U.S. Securities and Exchange Commission (SEC) regulation that provides an exemption from full securities registration, allowing companies—especially smaller issuers—to raise limited amounts of capital from the public with simplified disclosure requirements.
-
E.
REGN
REGN is the stock ticker symbol for Regeneron Pharmaceuticals, a major U.S.-based biotechnology company known for developing innovative antibody-based therapies.
- 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: REG Triple: [Regen (district), vehicleRegistrationCode, REG]
Generated description
REG is the vehicle registration code assigned to the Regen district in Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: REG Target entity description: REG is the vehicle registration code assigned to the Regen district in Germany.
-
A.
Reg
Reg is a common shortened given name, typically derived from Reginald.
-
B.
Registry
The Registry is the administrative organ of the International Criminal Tribunal for Rwanda responsible for managing court services, legal support, and overall tribunal operations.
-
C.
Reg D
Reg D is a Federal Reserve regulation that governs reserve requirements and certain aspects of deposit accounts and transaction limitations for U.S. depository institutions.
-
D.
Reg A
Reg A is a U.S. Securities and Exchange Commission (SEC) regulation that provides an exemption from full securities registration, allowing companies—especially smaller issuers—to raise limited amounts of capital from the public with simplified disclosure requirements.
-
E.
REGN
REGN is the stock ticker symbol for Regeneron Pharmaceuticals, a major U.S.-based biotechnology company known for developing innovative antibody-based therapies.
- 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_69ca847b1b3081908f72bc932c17cc41 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98e695948190ab107fff38c57de7 |
completed | April 1, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14c6538b08190a9f81304214a876d |
completed | April 4, 2026, 5:37 p.m. |
| NEDg | Description generation | batch_69d14d44b7f08190b66fecb315b37535 |
completed | April 4, 2026, 5:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d14e0823e881908ed723d20f14789b |
completed | April 4, 2026, 5:44 p.m. |
Created at: March 30, 2026, 8:01 p.m.