Triple
T2752774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | INGRES relational database system |
E61025
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | Postgres |
E17669
|
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: Postgres | Statement: [INGRES relational database system, influenced, Postgres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Postgres Context triple: [INGRES relational database system, influenced, Postgres]
-
A.
PostgreSQL
chosen
PostgreSQL is a powerful open-source relational database management system known for its robustness, extensibility, and strong standards compliance.
-
B.
PostGIS
PostGIS is an open-source spatial database extender that adds robust geographic object support and spatial querying capabilities to PostgreSQL.
-
C.
PL/pgSQL
PL/pgSQL is PostgreSQL’s procedural extension of SQL that allows writing stored functions and triggers with control structures like variables, loops, and conditionals.
-
D.
MariaDB
MariaDB is an open-source relational database management system, forked from MySQL, known for its compatibility, performance, and community-driven development.
-
E.
SQL
SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
- 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_69ab4b7a85bc819094a349b84beb1f2c |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb6ed9c08190824d1866e198ef80 |
completed | March 7, 2026, 8:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbdb43d481909bf4e61840979c0a |
completed | March 10, 2026, 6:36 a.m. |
Created at: March 6, 2026, 9:56 p.m.