Triple
T1160091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Node.js |
E24471
|
entity |
| Predicate | writtenIn |
P12727
|
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: [Node.js, writtenIn, C]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: C Context triple: [Node.js, writtenIn, 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.
Terminal C
Terminal C is one of the main passenger terminals at Luis Muñoz Marín International Airport in Puerto Rico, serving commercial airline operations and traveler services.
-
C.
Terminal C
Terminal C is one of the main passenger terminals at New York City's LaGuardia Airport, serving numerous domestic flights and airlines.
-
D.
Terminal C
Terminal C is one of the passenger terminals at Dallas/Fort Worth International Airport, serving various domestic flights and airlines within the airport’s complex.
-
E.
Terminal C
Terminal C is one of the passenger terminals at Sheremetyevo International Airport in Moscow, serving as a hub for various international and domestic flights.
- 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_69a494060e148190abb42f971242c197 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bcaf3a9081908bad2eba74dffbc1 |
completed | March 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac6f15a9ac8190802f66f3699fbbe7 |
completed | March 7, 2026, 6:31 p.m. |
Created at: March 1, 2026, 7:45 p.m.