Triple
T853351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ralph Merkle |
E18435
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object |
Elxsi
Elxsi was a computer company known for developing high-performance minicomputers and multiprocessor systems in the late 20th century.
|
E99144
|
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: Elxsi | Statement: [Ralph Merkle, employer, Elxsi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elxsi Context triple: [Ralph Merkle, employer, Elxsi]
-
A.
Xicor
Xicor was a semiconductor company best known for designing and manufacturing non-volatile memory and analog integrated circuits.
-
B.
Onex
Onex is a suburban municipality in western Switzerland located just outside the city of Geneva.
-
C.
Ornex
Ornex is a small commune in the Ain department of eastern France, located near the Swiss border in the Pays de Gex region.
-
D.
Element AI
Element AI was a Montreal-based artificial intelligence company and research lab known for developing enterprise AI solutions and advancing deep learning research.
-
E.
Genesys
Genesys is a global customer experience and contact center technology company known for its cloud-based solutions that help businesses manage and optimize customer interactions.
- 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: Elxsi Triple: [Ralph Merkle, employer, Elxsi]
Generated description
Elxsi was a computer company known for developing high-performance minicomputers and multiprocessor systems in the late 20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elxsi Target entity description: Elxsi was a computer company known for developing high-performance minicomputers and multiprocessor systems in the late 20th century.
-
A.
Xicor
Xicor was a semiconductor company best known for designing and manufacturing non-volatile memory and analog integrated circuits.
-
B.
Onex
Onex is a suburban municipality in western Switzerland located just outside the city of Geneva.
-
C.
Ornex
Ornex is a small commune in the Ain department of eastern France, located near the Swiss border in the Pays de Gex region.
-
D.
Element AI
Element AI was a Montreal-based artificial intelligence company and research lab known for developing enterprise AI solutions and advancing deep learning research.
-
E.
Genesys
Genesys is a global customer experience and contact center technology company known for its cloud-based solutions that help businesses manage and optimize customer interactions.
- 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac389a44819093396a58d2afa700 |
completed | March 1, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a792a29f448190b96d934656e5399e |
completed | March 4, 2026, 2:02 a.m. |
| NEDg | Description generation | batch_69a79335d99c819098c72e7a86ad1130 |
completed | March 4, 2026, 2:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a793c612d88190bce254142bd75f67 |
completed | March 4, 2026, 2:07 a.m. |
Created at: March 1, 2026, 7:39 p.m.