Triple
T3418318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Ant |
E72062
|
entity |
| Predicate | supportsTask |
P15794
|
FINISHED |
| Object | jjtree |
E284604
|
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: jjtree | Statement: [Apache Ant, supportsTask, jjtree]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: jjtree Context triple: [Apache Ant, supportsTask, jjtree]
-
A.
JTS
JTS is a leading academic and spiritual center of Conservative Judaism, known for training rabbis, cantors, and scholars and for its influential research in Jewish studies.
-
B.
Backus–Naur Form
Backus–Naur Form is a formal notation used to define the syntax of programming languages and other formal grammars in a precise, structured way.
-
C.
GNU Bison
chosen
GNU Bison is a widely used parser generator that converts context-free grammars into C-based parsers, commonly employed in compilers and interpreters within the GNU ecosystem.
-
D.
shunting-yard algorithm
The shunting-yard algorithm is a method for parsing mathematical expressions and converting infix notation to postfix (or prefix) form using a stack-based procedure.
-
E.
GNU Flex
GNU Flex is a widely used open-source lexical analyzer generator that produces C-based scanners for tokenizing text according to user-defined patterns.
- 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_69ad85ad38e48190b7660c5118a35289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb92df1e48190bbf22a47e44579f1 |
completed | March 8, 2026, 6 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3546e710c8190a6b9523b77f6c893 |
completed | March 13, 2026, 12:03 a.m. |
Created at: March 8, 2026, 3:15 p.m.