Triple
T18230146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PROFIBUS and PROFINET International |
E436523
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
PI
PI is the global industry organization responsible for developing and promoting the PROFIBUS and PROFINET industrial communication standards.
|
E1313807
|
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: PI | Statement: [PROFIBUS and PROFINET International, alsoKnownAs, PI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PI Context triple: [PROFIBUS and PROFINET International, alsoKnownAs, PI]
-
A.
PI
PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
-
B.
PI
PI is a leading independent research institute in Waterloo, Canada, dedicated to advancing fundamental theoretical physics and fostering scientific collaboration and education.
-
C.
PIP
PIP is a Puerto Rican political party that advocates for the island’s full independence from the United States.
-
D.
PIP
PIP is a UK welfare benefit that helps disabled people or those with long-term health conditions cover the extra costs of daily living and mobility.
-
E.
PIK
PIK is a leading German research institute focused on analyzing the causes and impacts of climate change and developing strategies for sustainable solutions.
- 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: PI Triple: [PROFIBUS and PROFINET International, alsoKnownAs, PI]
Generated description
PI is the global industry organization responsible for developing and promoting the PROFIBUS and PROFINET industrial communication standards.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PI Target entity description: PI is the global industry organization responsible for developing and promoting the PROFIBUS and PROFINET industrial communication standards.
-
A.
PI
PI is a leading independent research institute in Waterloo, Canada, dedicated to advancing fundamental theoretical physics and fostering scientific collaboration and education.
-
B.
PI
PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
-
C.
PIP
PIP is a Puerto Rican political party that advocates for the island’s full independence from the United States.
-
D.
PIP
PIP is a UK welfare benefit that helps disabled people or those with long-term health conditions cover the extra costs of daily living and mobility.
-
E.
PIK
PIK is a leading German research institute focused on analyzing the causes and impacts of climate change and developing strategies for sustainable solutions.
- 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_69d8b9103a8081908bbb0836fef10efd |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f4b284a8819088456c2af5fa1de5 |
completed | April 19, 2026, 3:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03ac66d0b08190a09a20b1f393ed4f |
completed | May 12, 2026, 10:40 p.m. |
| NEDg | Description generation | batch_6a03ad5a19208190942d0a2a92641f69 |
completed | May 12, 2026, 10:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03adfb5e148190b96a0026a72d789b |
completed | May 12, 2026, 10:47 p.m. |
Created at: April 10, 2026, 10:33 a.m.