Triple
T137736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walter Chrysler |
E2784
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Walter
Walter is a masculine given name of Germanic origin that has been widely used in English-speaking countries.
|
E32053
|
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: Walter | Statement: [Walter Chrysler, givenName, Walter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Walter Context triple: [Walter Chrysler, givenName, Walter]
-
A.
Harold
Harold is a masculine given name of Old English origin, historically borne by several notable figures including kings and modern public personalities.
-
B.
Wallace
Wallace is a volume series of early United States Supreme Court case reports compiled by reporter John William Wallace, later incorporated into the official United States Reports.
-
C.
Wallace
Wallace is a notable figure who succeeded Black in a position of leadership or prominence, likely within a political or organizational context.
-
D.
Edwin
Edwin is a masculine given name of Old English origin meaning "rich friend" or "prosperous friend."
-
E.
Oliver Wallace
Oliver Wallace was a British-born American composer and conductor best known for scoring numerous classic Disney animated films in the mid-20th 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: Walter Triple: [Walter Chrysler, givenName, Walter]
Generated description
Walter is a masculine given name of Germanic origin that has been widely used in English-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Walter Target entity description: Walter is a masculine given name of Germanic origin that has been widely used in English-speaking countries.
-
A.
Harold
Harold is a masculine given name of Old English origin, historically borne by several notable figures including kings and modern public personalities.
-
B.
Wallace
Wallace is a volume series of early United States Supreme Court case reports compiled by reporter John William Wallace, later incorporated into the official United States Reports.
-
C.
Wallace
Wallace is a notable figure who succeeded Black in a position of leadership or prominence, likely within a political or organizational context.
-
D.
Edwin
Edwin is a masculine given name of Old English origin meaning "rich friend" or "prosperous friend."
-
E.
Oliver Wallace
Oliver Wallace was a British-born American composer and conductor best known for scoring numerous classic Disney animated films in the mid-20th 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257a6cab88190944c8f74d8d1605c |
completed | Feb. 28, 2026, 2:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3736b2ff081909a5b36a93fa16130 |
completed | Feb. 28, 2026, 10:59 p.m. |
| NEDg | Description generation | batch_69a3741398148190b33de6d93ccc0725 |
completed | Feb. 28, 2026, 11:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3746ba85c8190bd45c2a5717317dc |
completed | Feb. 28, 2026, 11:04 p.m. |
Created at: Feb. 28, 2026, 2:31 a.m.