Triple
T16807178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeep Wrangler |
E408509
|
entity |
| Predicate | generation |
P4860
|
FINISHED |
| Object |
TJ (second generation)
TJ (second generation) is the second-generation Jeep Wrangler model, produced from the late 1990s to mid-2000s, known for reintroducing coil-spring suspension and refining the classic off-road SUV’s comfort and handling.
|
E1234570
|
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: TJ (second generation) | Statement: [Jeep Wrangler, generation, TJ (second generation)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TJ (second generation) Context triple: [Jeep Wrangler, generation, TJ (second generation)]
-
A.
TJ
TJ is the two-letter ISO 3166-1 alpha-2 country code assigned to Tajikistan.
-
B.
TJ
TJ is a software developer best known for creating the Node.js web framework Express and numerous other open-source tools.
-
C.
TJ
TJ is the commonly used abbreviation for TransJakarta, Jakarta’s bus rapid transit system.
-
D.
TJH
TJH is the three-letter IATA airport code assigned to Tajima Airport in Japan.
-
E.
T-Modell
The T-Modell is the station wagon variant of Mercedes-Benz passenger cars, offering extended cargo space and practicality compared to the sedan versions.
- 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: TJ (second generation) Triple: [Jeep Wrangler, generation, TJ (second generation)]
Generated description
TJ (second generation) is the second-generation Jeep Wrangler model, produced from the late 1990s to mid-2000s, known for reintroducing coil-spring suspension and refining the classic off-road SUV’s comfort and handling.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TJ (second generation) Target entity description: TJ (second generation) is the second-generation Jeep Wrangler model, produced from the late 1990s to mid-2000s, known for reintroducing coil-spring suspension and refining the classic off-road SUV’s comfort and handling.
-
A.
TJ
TJ is the two-letter ISO 3166-1 alpha-2 country code assigned to Tajikistan.
-
B.
TJ
TJ is a software developer best known for creating the Node.js web framework Express and numerous other open-source tools.
-
C.
TJ
TJ is the commonly used abbreviation for TransJakarta, Jakarta’s bus rapid transit system.
-
D.
TJH
TJH is the three-letter IATA airport code assigned to Tajima Airport in Japan.
-
E.
T-Modell
The T-Modell is the station wagon variant of Mercedes-Benz passenger cars, offering extended cargo space and practicality compared to the sedan versions.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2cd1e8c8190a7a05ba255f711c7 |
completed | April 18, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b28f125481909664363904f4c031 |
completed | May 10, 2026, 4:30 p.m. |
| NEDg | Description generation | batch_6a00b399786c8190acbd188ab55b1fa0 |
completed | May 10, 2026, 4:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00b46ab0608190bc59abb99842e6d4 |
completed | May 10, 2026, 4:38 p.m. |
Created at: April 10, 2026, 5:22 a.m.