Triple
T19834743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2005 Nalchik raid |
E476557
|
entity |
| Predicate | opposingForce |
P4567
|
FINISHED |
| Object |
OMON units
OMON units are Russian special police forces known for their heavily armed riot control, counterterrorism, and high-risk security operations.
|
E1397624
|
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: OMON units | Statement: [2005 Nalchik raid, opposingForce, OMON units]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OMON units Context triple: [2005 Nalchik raid, opposingForce, OMON units]
-
A.
OM10
OM10 is the station code assigned to Jiyugaoka Station on the Tokyu railway network in Tokyo, Japan.
-
B.
ONM
ONM is the IATA airport code for Socorro Municipal Airport in Socorro, New Mexico, United States.
-
C.
ONN
ONN is the acronym for Cuba’s National Office of Standardization, the state body responsible for developing and overseeing national standards and quality regulations.
-
D.
ONMU
ONMU is the commonly used abbreviation for Odesa National Maritime University, a higher education institution in Ukraine specializing in maritime studies and related fields.
-
E.
UNOMIG
UNOMIG was a United Nations peacekeeping mission established to monitor the ceasefire and support conflict resolution efforts in the Georgian–Abkhaz conflict.
- 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: OMON units Triple: [2005 Nalchik raid, opposingForce, OMON units]
Generated description
OMON units are Russian special police forces known for their heavily armed riot control, counterterrorism, and high-risk security operations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: OMON units Target entity description: OMON units are Russian special police forces known for their heavily armed riot control, counterterrorism, and high-risk security operations.
-
A.
OM10
OM10 is the station code assigned to Jiyugaoka Station on the Tokyu railway network in Tokyo, Japan.
-
B.
ONM
ONM is the IATA airport code for Socorro Municipal Airport in Socorro, New Mexico, United States.
-
C.
ONN
ONN is the acronym for Cuba’s National Office of Standardization, the state body responsible for developing and overseeing national standards and quality regulations.
-
D.
ONMU
ONMU is the commonly used abbreviation for Odesa National Maritime University, a higher education institution in Ukraine specializing in maritime studies and related fields.
-
E.
UNOMIG
UNOMIG was a United Nations peacekeeping mission established to monitor the ceasefire and support conflict resolution efforts in the Georgian–Abkhaz conflict.
- 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e656d0e738819093000d3307962328 |
completed | April 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07ccd9b71c81909002a92d0e826510 |
completed | May 16, 2026, 1:48 a.m. |
| NEDg | Description generation | batch_6a07cfa7121881908e3256a035f0fffb |
completed | May 16, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07d0aacc1481908f9b4d96ce34d88b |
completed | May 16, 2026, 2:04 a.m. |
Created at: April 10, 2026, 1:50 p.m.