Triple
T2752716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Smalltalk |
E61024
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | Lua |
E95187
|
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: Lua | Statement: [Smalltalk, influenced, Lua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lua Context triple: [Smalltalk, influenced, Lua]
-
A.
Lua
chosen
Lua is a lightweight, embeddable scripting language widely used for game development, configuration, and extending applications.
-
B.
Mono language
Mono language is a Native American Uto-Aztecan language traditionally spoken by the Mono people of eastern California.
-
C.
Elm
Elm is a civil parish and village in Cambridgeshire, England, known for its rural character and historic church.
-
D.
Elm
Elm is a statically typed, functional programming language that compiles to JavaScript and is designed for building reliable, maintainable web front-end applications.
-
E.
Julia
Julia is a high-level, high-performance programming language designed for numerical computing, data science, and scientific research, combining the ease of dynamic languages with the speed of compiled languages.
- 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.