Triple
T3426959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Google Search |
E72248
|
entity |
| Predicate | availableLanguageCount |
P36891
|
FINISHED |
| Object | 100+ |
—
|
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: 100+ | Statement: [Google Search, availableLanguageCount, 100+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: availableLanguageCount Context triple: [Google Search, availableLanguageCount, 100+]
-
A.
currentNumberOfLanguages
chosen
Indicates the present count of distinct languages associated with or used by a given entity.
-
B.
estimatedNumberOfLanguages
Indicates the approximate count of distinct languages associated with an entity, typically based on estimation rather than an exact measurement.
-
C.
hasApproximateNumberOfLanguages
Indicates that an entity is associated with a quantity representing an estimated or non-exact count of languages.
-
D.
hasLanguages
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
E.
originalNumberOfLanguages
Indicates the initial count of distinct languages associated with an entity before any changes or reductions occur.
- 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_69ad85ae14308190bcbc25cfa0246c0b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb982792c8190b1163eee4252210f |
completed | March 8, 2026, 6:01 p.m. |
| PD | Predicate disambiguation | batch_69adadfea024819094b41a13bc004bda |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:15 p.m.