Triple
T3429914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Checkmarx |
E72311
|
entity |
| Predicate | offersProduct |
P10882
|
FINISHED |
| Object |
SCA
SCA is a software composition analysis solution that identifies and manages vulnerabilities and license risks in open-source components used within applications.
|
E356953
|
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: SCA | Statement: [Checkmarx, offersProduct, SCA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SCA Context triple: [Checkmarx, offersProduct, SCA]
-
A.
SCA
SCA is a U.S. federal law that governs the privacy and disclosure of stored electronic communications and related data held by service providers.
-
B.
SCA
SCA is the National Rail station code for Scarborough railway station in North Yorkshire, England.
-
C.
SCAR
SCAR is an international scientific body that coordinates and promotes research in and about Antarctica and the Southern Ocean.
-
D.
SCMA
SCMA is the Smith College Museum of Art, a prominent academic art museum known for its diverse collections and educational programs in Northampton, Massachusetts.
-
E.
SCS
SCS is Carnegie Mellon University's renowned School of Computer Science, recognized globally for pioneering research and education in computing and related fields.
- 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: SCA Triple: [Checkmarx, offersProduct, SCA]
Generated description
SCA is a software composition analysis solution that identifies and manages vulnerabilities and license risks in open-source components used within applications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SCA Target entity description: SCA is a software composition analysis solution that identifies and manages vulnerabilities and license risks in open-source components used within applications.
-
A.
SCA
SCA is the National Rail station code for Scarborough railway station in North Yorkshire, England.
-
B.
SCA
SCA is a U.S. federal law that governs the privacy and disclosure of stored electronic communications and related data held by service providers.
-
C.
SCAR
SCAR is an international scientific body that coordinates and promotes research in and about Antarctica and the Southern Ocean.
-
D.
SCMA
SCMA is the Smith College Museum of Art, a prominent academic art museum known for its diverse collections and educational programs in Northampton, Massachusetts.
-
E.
SCS
SCS is Carnegie Mellon University's renowned School of Computer Science, recognized globally for pioneering research and education in computing and related fields.
- 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_69ad85ae14308190bcbc25cfa0246c0b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb9bd61908190a7bdd01f24334fc3 |
completed | March 8, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3547b1b3481909646bf36e8461ff4 |
completed | March 13, 2026, 12:04 a.m. |
| NEDg | Description generation | batch_69b35547a9a881909d754625806cff2a |
completed | March 13, 2026, 12:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b355efa89c8190bf9b2eb3c41257b3 |
completed | March 13, 2026, 12:10 a.m. |
Created at: March 8, 2026, 3:15 p.m.