Triple
T5430207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Honeywell F124 |
E121466
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
F124
F124 is a compact, high-performance turbofan engine developed by Honeywell for use in military trainer and light attack aircraft.
|
E519588
|
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: F124 | Statement: [Honeywell F124, abbreviation, F124]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: F124 Context triple: [Honeywell F124, abbreviation, F124]
-
A.
F24
F24 was the pennant number of HMS Maori, a British Tribal-class destroyer that served with distinction in the Royal Navy during World War II.
-
B.
F82
F82 is the pennant number assigned to HMS Sikh, a British Royal Navy Tribal-class destroyer that served during the Second World War.
-
C.
H125
The H125 is a popular single-engine light utility helicopter widely used worldwide for missions such as aerial work, passenger transport, and law enforcement.
-
D.
F1–F22
F1–F22 is a group of passenger boarding gates located in Terminal 3 of San Francisco International Airport.
-
E.
F-2
F-2 is a three-quarter-ton model in Ford’s first-generation postwar F-Series pickup truck lineup, positioned between the lighter F-1 and heavier F-3 trucks.
- 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: F124 Triple: [Honeywell F124, abbreviation, F124]
Generated description
F124 is a compact, high-performance turbofan engine developed by Honeywell for use in military trainer and light attack aircraft.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: F124 Target entity description: F124 is a compact, high-performance turbofan engine developed by Honeywell for use in military trainer and light attack aircraft.
-
A.
F24
F24 was the pennant number of HMS Maori, a British Tribal-class destroyer that served with distinction in the Royal Navy during World War II.
-
B.
F82
F82 is the pennant number assigned to HMS Sikh, a British Royal Navy Tribal-class destroyer that served during the Second World War.
-
C.
H125
The H125 is a popular single-engine light utility helicopter widely used worldwide for missions such as aerial work, passenger transport, and law enforcement.
-
D.
F1–F22
F1–F22 is a group of passenger boarding gates located in Terminal 3 of San Francisco International Airport.
-
E.
F-2
F-2 is a three-quarter-ton model in Ford’s first-generation postwar F-Series pickup truck lineup, positioned between the lighter F-1 and heavier F-3 trucks.
- 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_69bd463c65f0819082ee6483ab4b466a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd883e5e10819091e159dfd245e94d |
completed | March 20, 2026, 5:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf3ac6285081909afa6e91a023f6d5 |
completed | March 22, 2026, 12:41 a.m. |
| NEDg | Description generation | batch_69bf3c43ffe88190b8d2a10ea8a9a455 |
completed | March 22, 2026, 12:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf3ce7d6388190a9cd22f76f4420e0 |
completed | March 22, 2026, 12:50 a.m. |
Created at: March 20, 2026, 2:06 p.m.