Triple
T9687297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Castleton Corners |
E234442
|
entity |
| Predicate | servedByExpressBusRoute |
P39381
|
FINISHED |
| Object |
SIM32
SIM32 is a New York City express bus route that provides commuter service between Staten Island and Manhattan.
|
E814771
|
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: SIM32 | Statement: [Castleton Corners, servedByExpressBusRoute, SIM32]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SIM32 Context triple: [Castleton Corners, servedByExpressBusRoute, SIM32]
-
A.
SIM30
SIM30 is a New York City express bus route that provides commuter service between Staten Island and Manhattan.
-
B.
SIM31
SIM31 is an express bus route in New York City that provides commuter service between Staten Island and Manhattan.
-
C.
SIM4C
SIM4C is an express bus route in New York City that provides commuter service between Staten Island and Manhattan.
-
D.
SIM
SIM (Subscriber Identity Module) is a secure smart card or embedded chip used in mobile devices to store subscriber credentials and enable authentication and access to cellular networks.
-
E.
SIM
SIM is the commonly used abbreviation for the Science and Industry Museum in Manchester, a major UK museum dedicated to the history and impact of science, technology, and industry.
- 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: SIM32 Triple: [Castleton Corners, servedByExpressBusRoute, SIM32]
Generated description
SIM32 is a New York City express bus route that provides commuter service between Staten Island and Manhattan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SIM32 Target entity description: SIM32 is a New York City express bus route that provides commuter service between Staten Island and Manhattan.
-
A.
SIM30
SIM30 is a New York City express bus route that provides commuter service between Staten Island and Manhattan.
-
B.
SIM31
SIM31 is an express bus route in New York City that provides commuter service between Staten Island and Manhattan.
-
C.
SIM4C
SIM4C is an express bus route in New York City that provides commuter service between Staten Island and Manhattan.
-
D.
SIM
SIM (Subscriber Identity Module) is a secure smart card or embedded chip used in mobile devices to store subscriber credentials and enable authentication and access to cellular networks.
-
E.
SIM
SIM is the commonly used abbreviation for the Science and Industry Museum in Manchester, a major UK museum dedicated to the history and impact of science, technology, and industry.
- 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_69ca84ca73208190957a900c8543bdcc |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9cd42cc081909abcf4c85592d950 |
completed | April 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1910f94048190bf72712ed55e355b |
completed | April 4, 2026, 10:30 p.m. |
| NEDg | Description generation | batch_69d19327f0b481908be85bcb0deccb46 |
completed | April 4, 2026, 10:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d193fac390819092dd913dc78e2841 |
completed | April 4, 2026, 10:43 p.m. |
Created at: March 30, 2026, 8:17 p.m.