Triple
T199171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ford family (controlling shareholders) |
E4063
|
entity |
| Predicate | generationCount |
P8002
|
FINISHED |
| Object | multiple generations |
—
|
LITERAL FINISHED |
How this triple was built (2 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: multiple generations | Statement: [Ford family (controlling shareholders), generationCount, multiple generations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: generationCount Context triple: [Ford family (controlling shareholders), generationCount, multiple generations]
-
A.
generation
Indicates the relationship in which one entity produces, creates, or brings another entity into existence.
-
B.
numberBuilt
Indicates the total count of items or structures that have been constructed or produced.
-
C.
dominantGeneration
Indicates that one generation in a life cycle is more prominent, long-lived, or visually conspicuous than the other generation(s).
-
D.
branchCount
Indicates the number of branches associated with a given entity or structure.
-
E.
sessionCount
Indicates the number of distinct sessions associated with an entity or interaction context.
- F. None of above. chosen
Provenance (4 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25bcc6dc88190b8c24b485588dfe4 |
completed | Feb. 28, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69a25b4886b48190b46fd2244648a098 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25bc6ba208190aa8bec59d32f95fd |
completed | Feb. 28, 2026, 3:06 a.m. |
Created at: Feb. 28, 2026, 2:44 a.m.