Triple
T16907691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | cweave |
E424604
|
entity |
| Predicate | supportsProgrammingLanguage |
P1592
|
FINISHED |
| Object | C |
E9269
|
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: C | Statement: [cweave, supportsProgrammingLanguage, C]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: C Context triple: [cweave, supportsProgrammingLanguage, C]
-
A.
C
chosen
C is a foundational, general-purpose programming language known for its efficiency, low-level memory access, and influence on many later languages such as C++, Java, and Python.
-
B.
C
C is a local service on the New York City Subway that runs along the Eighth Avenue Line in Manhattan and continues through Brooklyn.
-
C.
C
C is the New York Stock Exchange ticker symbol for Citigroup Inc., a major global financial services and banking corporation.
-
D.
C
C is one of the three central women in Edward Albee’s play "Three Tall Women," representing a younger stage of the protagonist’s life and perspective.
-
E.
C
C is a Copenhagen S-train commuter rail line that runs through central Copenhagen and connects key suburban areas in the metropolitan network.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3ca3b1ad081908a032bff9699f2fe |
completed | April 18, 2026, 6:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c7b98d5c8190b61de47b246549e3 |
completed | May 10, 2026, 6 p.m. |
Created at: April 10, 2026, 5:30 a.m.