Triple
T21278704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Datsun Cherry |
E524457
|
entity |
| Predicate | generation |
P4860
|
FINISHED |
| Object |
F10
The F10 is a mid-1970s generation of the Datsun Cherry subcompact car, notable for being one of Nissan’s early front-wheel-drive models.
|
E1476776
|
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: F10 | Statement: [Datsun Cherry, generation, F10]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: F10 Context triple: [Datsun Cherry, generation, F10]
-
A.
F100
F100 is the ICAO aircraft type designator for the Fokker 100, a medium-sized twin-turbofan regional jet airliner.
-
B.
F107
The F107 is a small turbofan engine developed by Williams International, best known for powering various cruise missiles and unmanned aerial vehicles.
-
C.
H10
H10 is the shorthand name for Hilbert’s tenth problem, a famous decision problem in number theory concerning the solvability of Diophantine equations.
-
D.
T10
T10 is a TrawsCymru long-distance bus route that provides interurban public transport service in Wales.
-
E.
T10
T10 is a technical committee under INCITS responsible for developing standards for SCSI (Small Computer System Interface) and related storage interfaces.
- 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: F10 Triple: [Datsun Cherry, generation, F10]
Generated description
The F10 is a mid-1970s generation of the Datsun Cherry subcompact car, notable for being one of Nissan’s early front-wheel-drive models.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: F10 Target entity description: The F10 is a mid-1970s generation of the Datsun Cherry subcompact car, notable for being one of Nissan’s early front-wheel-drive models.
-
A.
F100
F100 is the ICAO aircraft type designator for the Fokker 100, a medium-sized twin-turbofan regional jet airliner.
-
B.
F107
The F107 is a small turbofan engine developed by Williams International, best known for powering various cruise missiles and unmanned aerial vehicles.
-
C.
H10
H10 is the shorthand name for Hilbert’s tenth problem, a famous decision problem in number theory concerning the solvability of Diophantine equations.
-
D.
T10
T10 is a TrawsCymru long-distance bus route that provides interurban public transport service in Wales.
-
E.
T10
T10 is a technical committee under INCITS responsible for developing standards for SCSI (Small Computer System Interface) and related storage interfaces.
- 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73658f598819098a1192abfa40a12 |
completed | April 21, 2026, 8:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0997ff61748190b8acf7b0f8a49372 |
completed | May 17, 2026, 10:27 a.m. |
| NEDg | Description generation | batch_6a0999bbbf408190b62ce8fe1e724c2c |
completed | May 17, 2026, 10:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a099a2edafc8190bc70076bbd8d4f09 |
completed | May 17, 2026, 10:36 a.m. |
Created at: April 16, 2026, 4:02 p.m.