Triple
T2476105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marine Corps Support Facility New Orleans |
E55091
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object |
MCSF-NOLA
MCSF-NOLA is a U.S. Marine Corps installation in New Orleans that serves as a major support and administrative hub for Marine forces in the region.
|
E270454
|
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: MCSF-NOLA | Statement: [Marine Corps Support Facility New Orleans, hasAbbreviation, MCSF-NOLA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MCSF-NOLA Context triple: [Marine Corps Support Facility New Orleans, hasAbbreviation, MCSF-NOLA]
-
A.
MCO
MCO is the National Rail station code used to identify Oxford Road railway station in Manchester, England.
-
B.
MCO
MCO is the IATA airport code for Orlando International Airport, a major air travel hub serving the Orlando, Florida metropolitan area and its tourist attractions.
-
C.
MCO
MCO is the three-letter ISO 3166-1 alpha-3 country code assigned to the Principality of Monaco.
-
D.
Nola
Nola is an ancient town in southern Italy, historically significant in Roman times and known as the place where Emperor Augustus died.
-
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: MCSF-NOLA Triple: [Marine Corps Support Facility New Orleans, hasAbbreviation, MCSF-NOLA]
Generated description
MCSF-NOLA is a U.S. Marine Corps installation in New Orleans that serves as a major support and administrative hub for Marine forces in the region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MCSF-NOLA Target entity description: MCSF-NOLA is a U.S. Marine Corps installation in New Orleans that serves as a major support and administrative hub for Marine forces in the region.
-
A.
MCO
MCO is the National Rail station code used to identify Oxford Road railway station in Manchester, England.
-
B.
MCO
MCO is the IATA airport code for Orlando International Airport, a major air travel hub serving the Orlando, Florida metropolitan area and its tourist attractions.
-
C.
MCO
MCO is the three-letter ISO 3166-1 alpha-3 country code assigned to the Principality of Monaco.
-
D.
Nola
Nola is an ancient town in southern Italy, historically significant in Roman times and known as the place where Emperor Augustus died.
-
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_69ab49e279e88190ab10d7248aea9d11 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd14c8c388190bbdc486ffed6899e |
completed | March 7, 2026, 7:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af17ab837881909bf8704acf9598e4 |
completed | March 9, 2026, 6:55 p.m. |
| NEDg | Description generation | batch_69af1a8c7784819088be431513d60325 |
completed | March 9, 2026, 7:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af1b10738881909b296ecd3ff53c1b |
completed | March 9, 2026, 7:10 p.m. |
Created at: March 6, 2026, 9:45 p.m.