Triple
T2952163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lubrizol |
E79843
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object |
LZ
LZ is the stock ticker symbol for The Lubrizol Corporation, a specialty chemicals company known for its lubricant additives and advanced materials.
|
E313832
|
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: LZ | Statement: [Lubrizol, tickerSymbol, LZ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LZ Context triple: [Lubrizol, tickerSymbol, LZ]
-
A.
LZA
LZA is the regional vehicle registration code assigned to motor vehicles registered in the city of Zamość in Poland.
-
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.
LK
LK is the two-letter ISO 3166-1 alpha-2 country code assigned to Sri Lanka for international standardization and identification purposes.
-
D.
LJ
LJ is the third-generation model of the Holden Torana, a compact Australian car produced in the early 1970s and known for its performance-oriented variants.
-
E.
ZLP
ZLP is the IATA station code for Zürich Hauptbahnhof, the main railway station in Zurich, Switzerland.
- 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: LZ Triple: [Lubrizol, tickerSymbol, LZ]
Generated description
LZ is the stock ticker symbol for The Lubrizol Corporation, a specialty chemicals company known for its lubricant additives and advanced materials.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LZ Target entity description: LZ is the stock ticker symbol for The Lubrizol Corporation, a specialty chemicals company known for its lubricant additives and advanced materials.
-
A.
LZA
LZA is the regional vehicle registration code assigned to motor vehicles registered in the city of Zamość in Poland.
-
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.
LK
LK is the two-letter ISO 3166-1 alpha-2 country code assigned to Sri Lanka for international standardization and identification purposes.
-
D.
LJ
LJ is the third-generation model of the Holden Torana, a compact Australian car produced in the early 1970s and known for its performance-oriented variants.
-
E.
ZLP
ZLP is the IATA station code for Zürich Hauptbahnhof, the main railway station in Zurich, Switzerland.
- 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_69ad8b1276588190a374a0b12e0f7bdf |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad98fcea5c8190b7d80de942bcb4f7 |
completed | March 8, 2026, 3:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fc7edc808190863ec8f99efa3875 |
completed | March 11, 2026, 5:24 a.m. |
| NEDg | Description generation | batch_69b0fd7d1cc88190a4f533a92d7e6de3 |
completed | March 11, 2026, 5:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b0fde74b608190b59da720c90adfeb |
completed | March 11, 2026, 5:30 a.m. |
Created at: March 8, 2026, 2:57 p.m.