Triple
T8474873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Line–Orange Line |
E200364
|
entity |
| Predicate | servesLine |
P839
|
FINISHED |
| Object | Green Line |
unclear NED1
|
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: Green Line | Statement: [Green Line–Orange Line, servesLine, Green Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green Line Context triple: [Green Line–Orange Line, servesLine, Green Line]
-
A.
Green Line
The Green Line is one of the main rapid transit routes of the Dubai Metro, serving key districts along Dubai Creek and connecting important commercial and residential areas.
-
B.
Green Line
Green Line is a hybrid-oriented trim level of the Saturn Aura midsize sedan designed to offer improved fuel efficiency and lower emissions.
-
C.
Green Line
The Green Line is one of the main rapid transit corridors of the Chennai Metro system in Chennai, India, connecting key areas of the city via elevated and underground stations.
-
D.
Green Line
The Green Line is one of the light rail routes in Houston’s METRORail system, serving the city’s East End and connecting it to downtown.
-
E.
Green Line
The Green Line is one of the color-coded rapid transit routes in the Washington Metro system, serving key neighborhoods in Washington, D.C. and parts of Maryland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69ca831a4f348190bfdd09250e86ae35 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4f89e0081909e74beb7c8f55653 |
completed | March 31, 2026, 3:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea813e384819090a8a7dc05e41ab2 |
completed | April 2, 2026, 5:32 p.m. |
Created at: March 30, 2026, 6:11 p.m.