Triple
T20317742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maassluis railway station |
E510421
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
Mss
Mss is the station code for Maassluis railway station in the Netherlands.
|
E1423628
|
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: Mss | Statement: [Maassluis railway station, hasStationCode, Mss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mss Context triple: [Maassluis railway station, hasStationCode, Mss]
-
A.
MSS
MSS is the Mobile Servicing System, a Canadian-built robotic arm and handling system used on the International Space Station for assembly, maintenance, and payload operations.
-
B.
MSSS
MSSS is the acronym for Quebec’s Ministry of Health and Social Services, the provincial government body responsible for overseeing public health care and social services.
-
C.
MSMS
MSMS is a graduate-level degree program focused on advanced studies in management, business strategy, and organizational leadership.
-
D.
MMSM
MMSM is the ICAO airport code assigned to Felipe Ángeles International Airport, a major commercial airport serving the Mexico City metropolitan area.
-
E.
MSH
MSH is the vehicle registration code for the Mansfeld-Südharz district in the German state of Saxony-Anhalt.
- 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: Mss Triple: [Maassluis railway station, hasStationCode, Mss]
Generated description
Mss is the station code for Maassluis railway station in the Netherlands.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mss Target entity description: Mss is the station code for Maassluis railway station in the Netherlands.
-
A.
MSS
MSS is the Mobile Servicing System, a Canadian-built robotic arm and handling system used on the International Space Station for assembly, maintenance, and payload operations.
-
B.
MSSS
MSSS is the acronym for Quebec’s Ministry of Health and Social Services, the provincial government body responsible for overseeing public health care and social services.
-
C.
MSMS
MSMS is a graduate-level degree program focused on advanced studies in management, business strategy, and organizational leadership.
-
D.
MMSM
MMSM is the ICAO airport code assigned to Felipe Ángeles International Airport, a major commercial airport serving the Mexico City metropolitan area.
-
E.
MSH
MSH is the vehicle registration code for the Mansfeld-Südharz district in the German state of Saxony-Anhalt.
- 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_69e0b4c7491c8190961113c4283b10b0 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e67788ca3c8190a3496fd54a5870d6 |
completed | April 20, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0861246f188190a0ffb84a61a7d8c1 |
completed | May 16, 2026, 12:20 p.m. |
| NEDg | Description generation | batch_6a08627316d48190ab6f2f2cedd9b1db |
completed | May 16, 2026, 12:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a086317d0888190809268c3b8b95f6f |
completed | May 16, 2026, 12:29 p.m. |
Created at: April 16, 2026, 11:19 a.m.