Triple
T4128144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federico Santa María Technical University |
E92773
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
USM
USM is the commonly used abbreviation for the Federico Santa María Technical University, a prominent Chilean institution known for its strong engineering and science programs.
|
E415947
|
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: USM | Statement: [Federico Santa María Technical University, shortName, USM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: USM Context triple: [Federico Santa María Technical University, shortName, USM]
-
A.
USM
USM (User-based Security Model) is the SNMPv3 security framework that provides user-level authentication, privacy (encryption), and access control for Simple Network Management Protocol communications.
-
B.
USMIA
USMIA is the UN/LOCODE identifier for the port and transport hub of Miami in the United States.
-
C.
UME
UME is Spain’s specialized military emergency unit responsible for rapid response to natural disasters, major accidents, and other civil emergencies.
-
D.
UM
UM is the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
-
E.
UM
UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
- 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: USM Triple: [Federico Santa María Technical University, shortName, USM]
Generated description
USM is the commonly used abbreviation for the Federico Santa María Technical University, a prominent Chilean institution known for its strong engineering and science programs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: USM Target entity description: USM is the commonly used abbreviation for the Federico Santa María Technical University, a prominent Chilean institution known for its strong engineering and science programs.
-
A.
USM
USM (User-based Security Model) is the SNMPv3 security framework that provides user-level authentication, privacy (encryption), and access control for Simple Network Management Protocol communications.
-
B.
USMIA
USMIA is the UN/LOCODE identifier for the port and transport hub of Miami in the United States.
-
C.
UME
UME is Spain’s specialized military emergency unit responsible for rapid response to natural disasters, major accidents, and other civil emergencies.
-
D.
UM
UM is the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
-
E.
UM
UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
- 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_69aed9685f70819086932777aec8d959 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69af021b17a08190b520101f54ec1e33 |
completed | March 9, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b576bc48f0819081ac3f921736854f |
completed | March 14, 2026, 2:54 p.m. |
| NEDg | Description generation | batch_69b5778a01e48190acb14d544cd53a63 |
completed | March 14, 2026, 2:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b577f2d46c8190a66b2b536088633c |
completed | March 14, 2026, 3 p.m. |
Created at: March 9, 2026, 3:42 p.m.