Triple
T1909477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International District/Chinatown Station |
E38075
|
entity |
| Predicate | fareSystem |
P395
|
FINISHED |
| Object |
ORCA
ORCA is a regional smart transit card system used for paying fares across multiple public transportation agencies in the Puget Sound region of Washington State.
|
E211950
|
NE FINISHED |
How this triple was built (4 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: ORCA | Statement: [International District/Chinatown Station, fareSystem, ORCA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ORCA Context triple: [International District/Chinatown Station, fareSystem, ORCA]
-
A.
Orcines
Orcines is a commune in central France’s Puy-de-Dôme department, known for its proximity to the Chaîne des Puys volcanic range.
-
B.
Orr
Orr is a surname of Scottish origin most famously associated with legendary Canadian ice hockey defenseman Bobby Orr.
-
C.
Orca (Quint's boat)
Orca is the small fishing boat owned by shark hunter Quint in the film "Jaws," used for the perilous hunt for the great white shark.
-
D.
ORC
ORC (Optimized Row Columnar) is a highly efficient, columnar storage file format commonly used in big data systems to enable fast analytics and compression.
-
E.
Orma
Orma is a major dialect of the Oromo language spoken primarily by the Orma people of Kenya.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ORCA Triple: [International District/Chinatown Station, fareSystem, ORCA]
Generated description
ORCA is a regional smart transit card system used for paying fares across multiple public transportation agencies in the Puget Sound region of Washington State.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ORCA Target entity description: ORCA is a regional smart transit card system used for paying fares across multiple public transportation agencies in the Puget Sound region of Washington State.
-
A.
Orcines
Orcines is a commune in central France’s Puy-de-Dôme department, known for its proximity to the Chaîne des Puys volcanic range.
-
B.
Orr
Orr is a surname of Scottish origin most famously associated with legendary Canadian ice hockey defenseman Bobby Orr.
-
C.
Orca (Quint's boat)
Orca is the small fishing boat owned by shark hunter Quint in the film "Jaws," used for the perilous hunt for the great white shark.
-
D.
ORC
ORC (Optimized Row Columnar) is a highly efficient, columnar storage file format commonly used in big data systems to enable fast analytics and compression.
-
E.
Orma
Orma is a major dialect of the Oromo language spoken primarily by the Orma people of Kenya.
- F. None of above. chosen
Provenance (5 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_69a8862a26088190aae5243695aeefc0 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1b7095c8190ad7e472aada30d3d |
completed | March 7, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeafdad3c8190be7aeaed8bdeac43 |
completed | March 8, 2026, 9:32 p.m. |
| NEDg | Description generation | batch_69adeb7075f48190a27b5039c3b4691e |
completed | March 8, 2026, 9:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adec37a4f88190961edf8f9c81773c |
completed | March 8, 2026, 9:37 p.m. |
Created at: March 4, 2026, 7:35 p.m.