Triple
T9399197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3.5L Duratec V6 |
E226422
|
entity |
| Predicate | marketedAs |
P1395
|
FINISHED |
| Object |
high-feature V6
The high-feature V6 is Ford’s advanced 3.5L Duratec V6 gasoline engine family, designed to deliver a balance of strong performance, refinement, and efficiency in a wide range of vehicles.
|
E796494
|
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: high-feature V6 | Statement: [3.5L Duratec V6, marketedAs, high-feature V6]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: high-feature V6 Context triple: [3.5L Duratec V6, marketedAs, high-feature V6]
-
A.
VIF
VIF is the commonly used abbreviation and nickname for Vålerenga Fotball, a Norwegian professional football club based in Oslo.
-
B.
HVF
HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
-
C.
VVO
VVO is the three-letter IATA airport code for Vladivostok International Airport in Russia.
-
D.
V-860
V-860 is a Soviet-era surface-to-air missile variant associated with the S-200 long-range air defense system.
-
E.
VFU
VFU is the commonly used abbreviation for Varna Free University, a private higher education institution in Varna, Bulgaria.
- 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: high-feature V6 Triple: [3.5L Duratec V6, marketedAs, high-feature V6]
Generated description
The high-feature V6 is Ford’s advanced 3.5L Duratec V6 gasoline engine family, designed to deliver a balance of strong performance, refinement, and efficiency in a wide range of vehicles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: high-feature V6 Target entity description: The high-feature V6 is Ford’s advanced 3.5L Duratec V6 gasoline engine family, designed to deliver a balance of strong performance, refinement, and efficiency in a wide range of vehicles.
-
A.
VIF
VIF is the commonly used abbreviation and nickname for Vålerenga Fotball, a Norwegian professional football club based in Oslo.
-
B.
HVF
HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
-
C.
VVO
VVO is the three-letter IATA airport code for Vladivostok International Airport in Russia.
-
D.
V-860
V-860 is a Soviet-era surface-to-air missile variant associated with the S-200 long-range air defense system.
-
E.
VFU
VFU is the commonly used abbreviation for Varna Free University, a private higher education institution in Varna, Bulgaria.
- 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_69ca843170f88190800a8ab2b5fc568e |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd51556fc08190b8ff8190a1485a3a |
completed | April 1, 2026, 5:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d10121f53c8190b4fe3ce0fe04aecc |
completed | April 4, 2026, 12:16 p.m. |
| NEDg | Description generation | batch_69d10321bbb881908569ae78bd516220 |
completed | April 4, 2026, 12:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1037738ec81909bc9518b898f8141 |
completed | April 4, 2026, 12:26 p.m. |
Created at: March 30, 2026, 7:46 p.m.