Triple
T31818906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hewlett-Packard garage in Palo Alto |
E812209
|
entity |
| Predicate | plaqueTextIncludes |
P105072
|
FINISHED |
| Object | Birthplace of Silicon Valley |
—
|
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: Birthplace of Silicon Valley | Statement: [Hewlett-Packard garage in Palo Alto, plaqueTextIncludes, Birthplace of Silicon Valley]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: plaqueTextIncludes Context triple: [Hewlett-Packard garage in Palo Alto, plaqueTextIncludes, Birthplace of Silicon Valley]
-
A.
hasPlaqueTextTopic
chosen
Indicates that the topic specified is the subject or main theme of the text written on a plaque.
-
B.
hasPlaquePlacement
Indicates that a plaque has been positioned or installed at a specific location or on a particular object.
-
C.
hasPlaque
Indicates that an entity possesses or displays a plaque, such as a commemorative plate or a deposit on a surface.
-
D.
hasNumberOfPlaques
Indicates the relationship that specifies how many plaques are associated with a given entity.
-
E.
hasLanguageOnPlaque
Indicates that a specific language appears in the text or inscription displayed on a particular plaque.
- F. None of above.
Provenance (3 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_69f348e97fa48190aa06286962af6dee |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6af7f24a881909e6ae0d937e90ea2 |
completed | May 3, 2026, 2:14 a.m. |
| PD | Predicate disambiguation | batch_69f6aca59d4881908d14ed47962703bd |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 30, 2026, 11:45 p.m.