Triple
T22663153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CATIA |
E559714
|
entity |
| Predicate | hasModule |
P12988
|
FINISHED |
| Object |
DMU Kinematics
DMU Kinematics is a CATIA module used to simulate, analyze, and validate the motion and mechanisms of digital mechanical assemblies in a virtual environment.
|
E1548583
|
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: DMU Kinematics | Statement: [CATIA, hasModule, DMU Kinematics]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DMU Kinematics Context triple: [CATIA, hasModule, DMU Kinematics]
-
A.
DMU
DMU is the IATA airport code for Dimapur Airport, which serves the city of Dimapur in the Indian state of Nagaland.
-
B.
dm_control
dm_control is a Python library and framework from DeepMind for building, running, and benchmarking physics-based reinforcement learning environments using the MuJoCo simulator.
-
C.
Kinetix
Kinetix is Rockwell Automation’s line of motion control products and servo drives used for precise, integrated industrial automation.
-
D.
Persona knee system
Persona knee system is a customized knee replacement implant line by Zimmer Biomet designed to closely match individual patient anatomy for improved fit and function.
-
E.
Kinet
Kinet is the high-speed alien home planet of the XLR8 species in the Ben 10 universe.
- 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: DMU Kinematics Triple: [CATIA, hasModule, DMU Kinematics]
Generated description
DMU Kinematics is a CATIA module used to simulate, analyze, and validate the motion and mechanisms of digital mechanical assemblies in a virtual environment.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DMU Kinematics Target entity description: DMU Kinematics is a CATIA module used to simulate, analyze, and validate the motion and mechanisms of digital mechanical assemblies in a virtual environment.
-
A.
DMU
DMU is the IATA airport code for Dimapur Airport, which serves the city of Dimapur in the Indian state of Nagaland.
-
B.
dm_control
dm_control is a Python library and framework from DeepMind for building, running, and benchmarking physics-based reinforcement learning environments using the MuJoCo simulator.
-
C.
Kinetix
Kinetix is Rockwell Automation’s line of motion control products and servo drives used for precise, integrated industrial automation.
-
D.
Persona knee system
Persona knee system is a customized knee replacement implant line by Zimmer Biomet designed to closely match individual patient anatomy for improved fit and function.
-
E.
Kinet
Kinet is the high-speed alien home planet of the XLR8 species in the Ben 10 universe.
- 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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f17660c0c88190bed9fa8f6517eec4 |
completed | April 29, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b73e409048190b435921e455d7bbd |
completed | May 18, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_6a0b7541919c8190a5bb0f31560bf871 |
completed | May 18, 2026, 8:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b762f315c8190a85d804e26a6b1c5 |
completed | May 18, 2026, 8:27 p.m. |
Created at: April 17, 2026, 3:08 p.m.