Triple
T6033913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ROOT |
E134370
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
TFile
TFile is a ROOT framework class that provides an interface for creating, reading, and writing ROOT data files used in high-energy physics and data analysis.
|
E565045
|
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: TFile | Statement: [ROOT, hasComponent, TFile]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TFile Context triple: [ROOT, hasComponent, TFile]
-
A.
Lisa File System
Lisa File System is the proprietary disk file system developed by Apple for its early Lisa computer, featuring a hierarchical directory structure and advanced metadata for its time.
-
B.
TIF
TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
-
C.
TIF
TIF is a major annual international trade fair held in Thessaloniki, Greece, showcasing products, services, and innovations from domestic and global exhibitors.
-
D.
Tiff
Tiff is a common shortened form of the given name Tiffany, often used as a casual or affectionate nickname.
-
E.
TAR
TAR is the ICAO airline designator assigned to Tunisair, the national flag carrier of Tunisia.
- 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: TFile Triple: [ROOT, hasComponent, TFile]
Generated description
TFile is a ROOT framework class that provides an interface for creating, reading, and writing ROOT data files used in high-energy physics and data analysis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TFile Target entity description: TFile is a ROOT framework class that provides an interface for creating, reading, and writing ROOT data files used in high-energy physics and data analysis.
-
A.
Lisa File System
Lisa File System is the proprietary disk file system developed by Apple for its early Lisa computer, featuring a hierarchical directory structure and advanced metadata for its time.
-
B.
TIF
TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
-
C.
TIF
TIF is a major annual international trade fair held in Thessaloniki, Greece, showcasing products, services, and innovations from domestic and global exhibitors.
-
D.
Tiff
Tiff is a common shortened form of the given name Tiffany, often used as a casual or affectionate nickname.
-
E.
TAR
TAR is the ICAO airline designator assigned to Tunisair, the national flag carrier of Tunisia.
- 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_69c0087515148190a97475d412563865 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056b220608190b156be95632cf3b3 |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11388aec881908408d5844c96ea2d |
completed | March 23, 2026, 10:18 a.m. |
| NEDg | Description generation | batch_69c11689c0788190847435b526572edc |
completed | March 23, 2026, 10:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c116eb349c81908cad0bf5ccc458bc |
completed | March 23, 2026, 10:33 a.m. |
Created at: March 22, 2026, 4:08 p.m.