Triple
T10246615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deportivo 18 de Marzo |
E240230
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
DMR
DMR is the station code for Deportivo 18 de Marzo, a Mexico City Metro station serving as an important transfer point in the network.
|
E853946
|
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: DMR | Statement: [Deportivo 18 de Marzo, hasStationCode, DMR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DMR Context triple: [Deportivo 18 de Marzo, hasStationCode, DMR]
-
A.
DMR
DMR is the commonly used abbreviation for the Dhaka Metro Rail, the rapid transit system serving Bangladesh’s capital city.
-
B.
DMR
DMR is a scientific instrument aboard the COBE satellite designed to measure tiny variations in the cosmic microwave background radiation across the sky.
-
C.
DCMR
DCMR is the official codified collection of all administrative rules and regulations issued by agencies of the District of Columbia government.
-
D.
DME
DME is a radio navigation system used in aviation to provide pilots with precise distance information from an aircraft to a ground-based station.
-
E.
DME
DME is the IATA airport code for Moscow’s Domodedovo International Airport, one of the major airports serving the Russian capital.
- 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: DMR Triple: [Deportivo 18 de Marzo, hasStationCode, DMR]
Generated description
DMR is the station code for Deportivo 18 de Marzo, a Mexico City Metro station serving as an important transfer point in the network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DMR Target entity description: DMR is the station code for Deportivo 18 de Marzo, a Mexico City Metro station serving as an important transfer point in the network.
-
A.
DMR
DMR is the commonly used abbreviation for the Dhaka Metro Rail, the rapid transit system serving Bangladesh’s capital city.
-
B.
DMR
DMR is a scientific instrument aboard the COBE satellite designed to measure tiny variations in the cosmic microwave background radiation across the sky.
-
C.
DCMR
DCMR is the official codified collection of all administrative rules and regulations issued by agencies of the District of Columbia government.
-
D.
DME
DME is a radio navigation system used in aviation to provide pilots with precise distance information from an aircraft to a ground-based station.
-
E.
DME
DME is the IATA airport code for Moscow’s Domodedovo International Airport, one of the major airports serving the Russian capital.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d22cfe1c8190afae178e11a59b8b |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7a597188190880200d13784f18f |
completed | April 9, 2026, 12:49 a.m. |
| NEDg | Description generation | batch_69d6fcab0bfc8190b47bc165ef3eb15d |
completed | April 9, 2026, 1:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d70fc3b15081908d1b67a7094c6210 |
completed | April 9, 2026, 2:32 a.m. |
Created at: April 6, 2026, 11:27 a.m.