Triple
T9914990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transilien Paris–Nord line H |
E185845
|
entity |
| Predicate | rollingStock |
P1305
|
FINISHED |
| Object | Z 50000 |
E613619
|
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: Z 50000 | Statement: [Transilien Paris–Nord line H, rollingStock, Z 50000]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Z 50000 Context triple: [Transilien Paris–Nord line H, rollingStock, Z 50000]
-
A.
Z 22500
Z 22500 is a class of French double-deck electric multiple unit trains operated by SNCF, primarily used for suburban commuter services around Paris.
-
B.
Z 50000 (Francilien)
chosen
The Z 50000, known as the Francilien, is a modern electric multiple unit train used for suburban and regional passenger services in the Île-de-France region around Paris.
-
C.
Z 5600
Z 5600 is a class of French electric multiple unit trains used for suburban passenger services in the Paris region.
-
D.
ZS
ZS is the vehicle registration code assigned to cars registered in the Polish city of Szczecin.
-
E.
ZS
ZS is the stock ticker symbol for Zscaler, a cloud-based information security company traded on the NASDAQ.
- 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_69ca829b45f481909040f7b99a1976ed |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb53d5be48190ab47e152760d5e2c |
completed | April 2, 2026, 12:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20dd82edc8190b405a3969864af77 |
completed | April 5, 2026, 7:23 a.m. |
Created at: March 30, 2026, 8:41 p.m.