Triple
T14929121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris-Saint-Lazare station |
E372209
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object | LAZ |
E650481
|
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: LAZ | Statement: [Paris-Saint-Lazare station, stationCode, LAZ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LAZ Context triple: [Paris-Saint-Lazare station, stationCode, LAZ]
-
A.
LAZ
chosen
LAZ is the station code for San Lázaro, a Mexico City Metro station serving Line 1 and Line B near the city’s eastern transport hubs.
-
B.
Laz
Laz is a South Caucasian (Kartvelian) language traditionally spoken by the Laz people along the southeastern Black Sea coast, particularly in northeastern Turkey and parts of Georgia.
-
C.
LAJ
LAJ is the station code for La Junta station, an Amtrak railroad stop in La Junta, Colorado, serving long-distance passenger trains.
-
D.
Lazi
Lazi is a coastal municipality on the southeastern side of Siquijor Island in the Philippines, known for its historic church, natural springs, and waterfalls.
-
E.
LZ
LZ is the stock ticker symbol for The Lubrizol Corporation, a specialty chemicals company known for its lubricant additives and advanced materials.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded634e67881909daec9eaef188d09 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72c728948190a1cfc62f4038b6ec |
completed | May 8, 2026, 11:33 p.m. |
Created at: April 10, 2026, 2:36 a.m.