Triple
T8401189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bay Terrace, Queens |
E198374
|
entity |
| Predicate | servedByBusRoute |
P14525
|
FINISHED |
| Object |
QM2
QM2 is an express bus route in New York City that provides commuter service between Queens and Manhattan.
|
E730702
|
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: QM2 | Statement: [Bay Terrace, Queens, servedByBusRoute, QM2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: QM2 Context triple: [Bay Terrace, Queens, servedByBusRoute, QM2]
-
A.
QGM
QGM is the post-nominal letters used to denote recipients of The Queen's Gallantry Medal, a British decoration awarded for exemplary acts of bravery.
-
B.
The QC
The QC is a popular nickname for Charlotte, North Carolina, reflecting its identity as a major financial and cultural hub in the southeastern United States.
-
C.
QMP
QMP (QEMU Machine Protocol) is a JSON-based control protocol that allows external programs to monitor and manage QEMU virtual machines programmatically.
-
D.
MQD
MQD is the station code for Metro Miguel Ángel de Quevedo, a stop on Mexico City’s metro system.
-
E.
MAQ
MAQ is the IATA airport code for Mae Sot Airport, which serves the town of Mae Sot in western Thailand near the Myanmar border.
- 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: QM2 Triple: [Bay Terrace, Queens, servedByBusRoute, QM2]
Generated description
QM2 is an express bus route in New York City that provides commuter service between Queens and Manhattan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: QM2 Target entity description: QM2 is an express bus route in New York City that provides commuter service between Queens and Manhattan.
-
A.
QGM
QGM is the post-nominal letters used to denote recipients of The Queen's Gallantry Medal, a British decoration awarded for exemplary acts of bravery.
-
B.
The QC
The QC is a popular nickname for Charlotte, North Carolina, reflecting its identity as a major financial and cultural hub in the southeastern United States.
-
C.
QMP
QMP (QEMU Machine Protocol) is a JSON-based control protocol that allows external programs to monitor and manage QEMU virtual machines programmatically.
-
D.
MQD
MQD is the station code for Metro Miguel Ángel de Quevedo, a stop on Mexico City’s metro system.
-
E.
MAQ
MAQ is the IATA airport code for Mae Sot Airport, which serves the town of Mae Sot in western Thailand near the Myanmar border.
- 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_69ca8310df9c8190b25f16161cca3e41 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb824da3148190bfa3a1abfdfa02de |
completed | March 31, 2026, 8:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cde8789a608190a3503f544a19d204 |
completed | April 2, 2026, 3:54 a.m. |
| NEDg | Description generation | batch_69cdebfd60188190a1681344e2bf1e9e |
completed | April 2, 2026, 4:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cded2fa42c8190bfbfc79caf38bf8e |
completed | April 2, 2026, 4:14 a.m. |
Created at: March 30, 2026, 6:04 p.m.