Triple
T6211267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Merced Regional Airport |
E138875
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
MCE
MCE is the three-letter IATA airport code for Merced Regional Airport in Merced, California.
|
E575611
|
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: MCE | Statement: [Merced Regional Airport, IATAcode, MCE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MCE Context triple: [Merced Regional Airport, IATAcode, MCE]
-
A.
MEC
MEC is the commonly used acronym for Uruguay’s Ministry of Education and Culture, the national body responsible for educational policy and cultural affairs.
-
B.
MEF
MEF is Italy’s Ministry of Economy and Finance, the government department responsible for national economic policy, public finances, and the state budget.
-
C.
MCP
MCP is a comprehensive regulatory framework in Massachusetts that governs the assessment and cleanup of contaminated sites to protect public health and the environment.
-
D.
MCD
MCD is a system of urban and suburban commuter rail lines in Moscow designed to function like an express metro, connecting the city with its surrounding regions.
-
E.
MCS
MCS is the Mellon College of Science, a core academic division of Carnegie Mellon University known for its programs in the natural and mathematical sciences.
- 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: MCE Triple: [Merced Regional Airport, IATAcode, MCE]
Generated description
MCE is the three-letter IATA airport code for Merced Regional Airport in Merced, California.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MCE Target entity description: MCE is the three-letter IATA airport code for Merced Regional Airport in Merced, California.
-
A.
MEC
MEC is the commonly used acronym for Uruguay’s Ministry of Education and Culture, the national body responsible for educational policy and cultural affairs.
-
B.
MEF
MEF is Italy’s Ministry of Economy and Finance, the government department responsible for national economic policy, public finances, and the state budget.
-
C.
MCP
MCP is a comprehensive regulatory framework in Massachusetts that governs the assessment and cleanup of contaminated sites to protect public health and the environment.
-
D.
MCD
MCD is a system of urban and suburban commuter rail lines in Moscow designed to function like an express metro, connecting the city with its surrounding regions.
-
E.
MCS
MCS is the Mellon College of Science, a core academic division of Carnegie Mellon University known for its programs in the natural and mathematical sciences.
- 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_69c008ada364819096c9e92c74d639b5 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0628adccc8190b94f5c2c1d5d03f7 |
completed | March 22, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16f57821c8190bd7a5f6bf286ab09 |
completed | March 23, 2026, 4:50 p.m. |
| NEDg | Description generation | batch_69c1bfe63d708190b734c064bdf0d735 |
completed | March 23, 2026, 10:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1c3f2b1388190bef6fe203f11fa3f |
completed | March 23, 2026, 10:51 p.m. |
Created at: March 22, 2026, 4:21 p.m.