Triple
T12160144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Convair F-102 Delta Dagger |
E289681
|
entity |
| Predicate | variant |
P4680
|
FINISHED |
| Object |
TF-102A
The TF-102A is a two-seat trainer version of the Convair F-102 Delta Dagger supersonic interceptor aircraft, used primarily for pilot instruction and transition training.
|
E967131
|
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: TF-102A | Statement: [Convair F-102 Delta Dagger, variant, TF-102A]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TF-102A Context triple: [Convair F-102 Delta Dagger, variant, TF-102A]
-
A.
Tianshou
Tianshou is a reinforcement learning library for PyTorch that provides modular, efficient tools and algorithms for training and evaluating RL agents.
-
B.
Tianshou
Tianshou was the brief era name proclaimed by Empress Wu Zetian during her rule in the late 7th century Tang China.
-
C.
Tian Wen
Tian Wen is an ancient Chinese poetic work traditionally attributed to Qu Yuan, known for its rich mythological imagery and philosophical questioning.
-
D.
Tian Rui
Tian Rui is a chapter of the classical Daoist text Liezi, traditionally attributed to the philosopher Lie Yukou.
-
E.
Tianyou
Tianyou was the final era name of the Tang dynasty, marking its last years before the dynasty’s collapse in the early 10th century.
- 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: TF-102A Triple: [Convair F-102 Delta Dagger, variant, TF-102A]
Generated description
The TF-102A is a two-seat trainer version of the Convair F-102 Delta Dagger supersonic interceptor aircraft, used primarily for pilot instruction and transition training.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TF-102A Target entity description: The TF-102A is a two-seat trainer version of the Convair F-102 Delta Dagger supersonic interceptor aircraft, used primarily for pilot instruction and transition training.
-
A.
Tianshou
Tianshou is a reinforcement learning library for PyTorch that provides modular, efficient tools and algorithms for training and evaluating RL agents.
-
B.
Tianshou
Tianshou was the brief era name proclaimed by Empress Wu Zetian during her rule in the late 7th century Tang China.
-
C.
Tian Wen
Tian Wen is an ancient Chinese poetic work traditionally attributed to Qu Yuan, known for its rich mythological imagery and philosophical questioning.
-
D.
Tian Rui
Tian Rui is a chapter of the classical Daoist text Liezi, traditionally attributed to the philosopher Lie Yukou.
-
E.
Tianyou
Tianyou was the final era name of the Tang dynasty, marking its last years before the dynasty’s collapse in the early 10th century.
- 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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915c395e48190a16e97fd29787a51 |
completed | April 10, 2026, 3:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f69e8498819080d571e6fb4edfde |
completed | May 2, 2026, 1:05 p.m. |
| NEDg | Description generation | batch_69f601e0777081909e1212436680a10d |
completed | May 2, 2026, 1:53 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f602a21f948190849839301f49d55a |
completed | May 2, 2026, 1:56 p.m. |
Created at: April 8, 2026, 9:50 p.m.