Triple
T8609289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flatpak |
E203877
|
entity |
| Predicate | supportsBuildManifestFormat |
P83855
|
FINISHED |
| Object | YAML |
E208075
|
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: YAML | Statement: [Flatpak, supportsBuildManifestFormat, YAML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: YAML Context triple: [Flatpak, supportsBuildManifestFormat, YAML]
-
A.
YAML
chosen
YAML is a human-readable data serialization language commonly used for configuration files and data exchange, emphasizing simplicity and ease of editing.
-
B.
TOML
TOML is a human-readable configuration file format designed for simplicity and unambiguous parsing, commonly used in modern software tooling and package managers.
-
C.
YAM
YAM is the IATA airport code for Sault Ste. Marie Airport in Ontario, Canada.
-
D.
JSON5
JSON5 is an extension of the JSON data format that adds more human-friendly features like comments, trailing commas, and unquoted object keys while remaining largely compatible with standard JSON.
-
E.
YANG
YANG is a data modeling language widely used in networking, particularly with NETCONF and RESTCONF, to define the structure and semantics of configuration and state data on network devices.
- 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_69ca832c23e4819095a9f3eea4a21828 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc5c2d852081908901f5d2a47035b0 |
completed | March 31, 2026, 11:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea90dd93081908140ac0ce23be820 |
completed | April 2, 2026, 5:36 p.m. |
Created at: March 30, 2026, 6:25 p.m.