Triple
T277225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SQL |
E5275
|
entity |
| Predicate | influencedBy |
P9
|
FINISHED |
| Object | SEQUEL |
E5275
|
NE FINISHED |
How this triple was built (2 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: SEQUEL | Statement: [SQL, influencedBy, SEQUEL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SEQUEL Context triple: [SQL, influencedBy, SEQUEL]
-
A.
SQL
chosen
SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
-
B.
PostgreSQL
PostgreSQL is a powerful open-source relational database management system known for its robustness, extensibility, and strong standards compliance.
-
C.
Siebel
Siebel is a surname most prominently associated with Jennifer Siebel Newsom, an American documentary filmmaker and the First Partner of California.
-
D.
Seedley
Seedley is a residential district within the city of Salford in Greater Manchester, England.
-
E.
Tableau
Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25ded68c88190b1fc595ce329aeb9 |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a391525a7081909166884a8ea47ace |
completed | March 1, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.