Triple
T13232764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | US1667641005 |
E315063
|
entity |
| Predicate | hasTickerSymbol |
P1447
|
FINISHED |
| Object | CVX |
E315062
|
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: CVX | Statement: [US1667641005, hasTickerSymbol, CVX]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CVX Context triple: [US1667641005, hasTickerSymbol, CVX]
-
A.
CVX
chosen
CVX is the stock ticker symbol for Chevron Corporation, a major American multinational energy and oil company.
-
B.
Convex Optimization
Convex Optimization is a widely used graduate-level textbook that systematically develops the theory, algorithms, and applications of convex optimization problems in engineering, statistics, and applied mathematics.
-
C.
SCIP
SCIP is the ICAO airport code for Mataveri International Airport, the main air gateway to Easter Island in Chile.
-
D.
Karush–Kuhn–Tucker conditions
The Karush–Kuhn–Tucker conditions are fundamental optimality criteria in nonlinear programming that generalize Lagrange multipliers to handle inequality constraints.
-
E.
CVK
CVK is a major campus of Charité – Universitätsmedizin Berlin, housing extensive clinical and research facilities in the Wedding district of Berlin.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d34ff288190bdb550a019b7a470 |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f17ba9081909929201be937c2cf |
completed | May 3, 2026, 10:10 a.m. |
Created at: April 9, 2026, 9:22 p.m.