Triple
T13780751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ARD |
E331124
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
ARD Text
ARD Text is the teletext service of the German public broadcaster ARD, providing news, information, and program details via television text pages.
|
E1062617
|
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: ARD Text | Statement: [ARD, hasPart, ARD Text]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ARD Text Context triple: [ARD, hasPart, ARD Text]
-
A.
Live Text
Live Text is a macOS and iOS feature that uses on-device intelligence to recognize and interact with text in photos, screenshots, and live camera views.
-
B.
C-text
C-text is one of the principal textual versions of the Middle English allegorical poem *Piers Plowman*, representing a distinct editorial and manuscript tradition within its complex transmission history.
-
C.
textutils
textutils was the former name of a collection of GNU command-line text processing utilities that were later consolidated into the GNU Core Utilities package.
-
D.
Metin
Metin is a common Turkish male given name used by various notable figures in Turkey.
-
E.
ARTIC
ARTIC is a major transportation hub in Anaheim, California, serving as an intermodal station for trains, buses, and other transit services.
- 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: ARD Text Triple: [ARD, hasPart, ARD Text]
Generated description
ARD Text is the teletext service of the German public broadcaster ARD, providing news, information, and program details via television text pages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ARD Text Target entity description: ARD Text is the teletext service of the German public broadcaster ARD, providing news, information, and program details via television text pages.
-
A.
Live Text
Live Text is a macOS and iOS feature that uses on-device intelligence to recognize and interact with text in photos, screenshots, and live camera views.
-
B.
C-text
C-text is one of the principal textual versions of the Middle English allegorical poem *Piers Plowman*, representing a distinct editorial and manuscript tradition within its complex transmission history.
-
C.
textutils
textutils was the former name of a collection of GNU command-line text processing utilities that were later consolidated into the GNU Core Utilities package.
-
D.
Metin
Metin is a common Turkish male given name used by various notable figures in Turkey.
-
E.
ARTIC
ARTIC is a major transportation hub in Anaheim, California, serving as an intermodal station for trains, buses, and other transit services.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02460a688190a27874f8d35819c7 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b079013881908e9f5412e5dfb0b2 |
completed | May 3, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69f7b1d9f0b481909752dca3f74a2211 |
completed | May 3, 2026, 8:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7b32db8808190a3dcdd0fe2ce368f |
completed | May 3, 2026, 8:42 p.m. |
Created at: April 9, 2026, 10:11 p.m.