Triple
T8720154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 1 (Paris Métro) |
E206989
|
entity |
| Predicate | hasRollingStock |
P1305
|
FINISHED |
| Object | MP 05 |
E206992
|
NE FINISHED |
How this triple was built (2 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 05 | Statement: [Line 1 (Paris Métro), hasRollingStock, MP 05]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MP 05 Context triple: [Line 1 (Paris Métro), hasRollingStock, MP 05]
-
A.
MP 05
chosen
MP 05 is a rubber-tyred, fully automated train model used on the Paris Métro, notably on Line 1 and Line 14.
-
B.
MP 43
MP 43 is an early German World War II assault rifle prototype that evolved into the famous Sturmgewehr 44, one of the first true assault rifles in history.
-
C.
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.
-
D.
MP-17
MP-17 is the regional vehicle registration code assigned to the Rewa district in the Indian state of Madhya Pradesh.
-
E.
MPM-10
MPM-10 is a modern rubber-tired metro train model used on the Montreal Metro, designed to increase capacity, comfort, and energy efficiency.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca835811d8819081ea00fd2a2c9a1c |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d02a52c81909f93622ae6920b80 |
completed | March 31, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf28f599a481908e93bc5b5c41296e |
completed | April 3, 2026, 2:41 a.m. |
Created at: March 30, 2026, 6:36 p.m.