Triple
T32818641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Line (Washington Metro) stations |
E839370
|
entity |
| Predicate | haveInformationSystem |
P68900
|
FINISHED |
| Object | real-time train arrival displays |
—
|
LITERAL 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: real-time train arrival displays | Statement: [Red Line (Washington Metro) stations, haveInformationSystem, real-time train arrival displays]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveInformationSystem Context triple: [Red Line (Washington Metro) stations, haveInformationSystem, real-time train arrival displays]
-
A.
hasTechnologySystem
Indicates that one entity possesses, utilizes, or is supported by a particular technology system.
-
B.
hasDigitalSystem
chosen
Indicates that an entity possesses, uses, or is equipped with a digital system (such as software, hardware, or an integrated digital platform).
-
C.
hadSystem
Indicates that an entity possessed, used, or was associated with a particular system.
-
D.
hasPASystem
Indicates that an entity possesses or is equipped with a public address (PA) system.
-
E.
systemUsedBy
Indicates that a particular system is utilized or operated by a specified agent or user.
- F. None of above.
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_69f3493df9008190a8f5d843dcd77704 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6ce6d659881909ddcec1d2966e020 |
completed | May 3, 2026, 4:26 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1667a48190b42684f6ec22dae9 |
completed | May 3, 2026, 4:16 a.m. |
Created at: May 1, 2026, 1:15 a.m.