Triple
T7490149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doppler effect |
E176983
|
entity |
| Predicate | usedIn |
P98
|
FINISHED |
| Object |
Doppler ultrasound
Doppler ultrasound is a medical imaging technique that uses high-frequency sound waves to measure and visualize blood flow within vessels and the heart.
|
E669200
|
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: Doppler ultrasound | Statement: [Doppler effect, usedIn, Doppler ultrasound]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doppler ultrasound Context triple: [Doppler effect, usedIn, Doppler ultrasound]
-
A.
USG
USG is a regional higher education center in Rockville, Maryland that hosts programs from multiple University System of Maryland institutions on a single shared campus.
-
B.
USG
USG is the acronym commonly used for the University System of Georgia, the public higher education system for the state of Georgia in the United States.
-
C.
ECT
ECT is the three-letter station code used by Transport for London to identify Earl's Court Underground station.
-
D.
MRA
MRA is the IATA airport code for Misrata International Airport, a commercial airport serving the city of Misrata in Libya.
-
E.
Proscan
Proscan is a consumer electronics brand known for producing affordable televisions and related audio-visual equipment.
- 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: Doppler ultrasound Triple: [Doppler effect, usedIn, Doppler ultrasound]
Generated description
Doppler ultrasound is a medical imaging technique that uses high-frequency sound waves to measure and visualize blood flow within vessels and the heart.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Doppler ultrasound Target entity description: Doppler ultrasound is a medical imaging technique that uses high-frequency sound waves to measure and visualize blood flow within vessels and the heart.
-
A.
USG
USG is a regional higher education center in Rockville, Maryland that hosts programs from multiple University System of Maryland institutions on a single shared campus.
-
B.
USG
USG is the acronym commonly used for the University System of Georgia, the public higher education system for the state of Georgia in the United States.
-
C.
ECT
ECT is the three-letter station code used by Transport for London to identify Earl's Court Underground station.
-
D.
MRA
MRA is the IATA airport code for Misrata International Airport, a commercial airport serving the city of Misrata in Libya.
-
E.
Proscan
Proscan is a consumer electronics brand known for producing affordable televisions and related audio-visual equipment.
- 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_69c69f2583808190bd1a4936c42a5815 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f55bdb3481908653b46eafba011d |
completed | March 27, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83c71f5748190bdda4cf9b8dfc6ea |
completed | March 28, 2026, 8:39 p.m. |
| NEDg | Description generation | batch_69c83e7b2ab08190a5ecb9b87af067a5 |
completed | March 28, 2026, 8:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c842bad1e8819093bf61d9480dbd22 |
completed | March 28, 2026, 9:06 p.m. |
Created at: March 27, 2026, 3:43 p.m.