Triple
T1841565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Metro |
E41186
|
entity |
| Predicate | hasRollingStock |
P1305
|
FINISHED |
| Object |
MP 14
MP 14 is a modern rubber-tyred train model used on the Paris Métro, designed for improved energy efficiency, automation, and passenger comfort.
|
E206993
|
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: MP 14 | Statement: [Paris Metro, hasRollingStock, MP 14]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MP 14 Context triple: [Paris Metro, hasRollingStock, MP 14]
-
A.
MK 2
MK 2 is the small natural satellite orbiting the distant dwarf planet Makemake in the Kuiper Belt.
-
B.
SR-143
SR-143 is a short Arizona state highway in the Phoenix metropolitan area that primarily serves as a connector between Interstate 10 and Phoenix Sky Harbor International Airport.
-
C.
MP
MP is the two-letter ISO 3166-1 alpha-2 country code assigned to the Northern Mariana Islands.
-
D.
MXP
MXP is the IATA airport code for Milan Malpensa Airport, the largest international airport serving the Milan metropolitan area in Italy.
-
E.
MPS
MPS is a leading German research institute specializing in the study of the Sun and the solar system, operating under the Max Planck Society.
- 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: MP 14 Triple: [Paris Metro, hasRollingStock, MP 14]
Generated description
MP 14 is a modern rubber-tyred train model used on the Paris Métro, designed for improved energy efficiency, automation, and passenger comfort.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MP 14 Target entity description: MP 14 is a modern rubber-tyred train model used on the Paris Métro, designed for improved energy efficiency, automation, and passenger comfort.
-
A.
MK 2
MK 2 is the small natural satellite orbiting the distant dwarf planet Makemake in the Kuiper Belt.
-
B.
SR-143
SR-143 is a short Arizona state highway in the Phoenix metropolitan area that primarily serves as a connector between Interstate 10 and Phoenix Sky Harbor International Airport.
-
C.
MP
MP is the two-letter ISO 3166-1 alpha-2 country code assigned to the Northern Mariana Islands.
-
D.
MXP
MXP is the IATA airport code for Milan Malpensa Airport, the largest international airport serving the Milan metropolitan area in Italy.
-
E.
MPS
MPS is a leading German research institute specializing in the study of the Sun and the solar system, operating under the Max Planck Society.
- 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb03e7a7481909c5b902034390ef1 |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adc9bb92a88190a00b102d3be0383c |
completed | March 8, 2026, 7:10 p.m. |
| NEDg | Description generation | batch_69adcaf078a0819082c4bb48a3820ada |
completed | March 8, 2026, 7:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adce8a68848190ab56f5df7311dbca |
completed | March 8, 2026, 7:31 p.m. |
Created at: March 4, 2026, 7:33 p.m.