Triple
T473365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Constitution |
E9006
|
entity |
| Predicate | wordCountApproximate |
P6006
|
FINISHED |
| Object | 90000 |
—
|
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: 90000 | Statement: [Texas Constitution, wordCountApproximate, 90000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wordCountApproximate Context triple: [Texas Constitution, wordCountApproximate, 90000]
-
A.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
-
B.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
C.
approximateNumberOfVerses
Indicates an estimated or approximate count of verses associated with an entity.
-
D.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
-
E.
lengthInWords
chosen
Indicates the number of words that make up the length of something, typically a text or expression.
- 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0208c788190a96cdabcf593fda7 |
completed | Feb. 28, 2026, 1:39 p.m. |
| PD | Predicate disambiguation | batch_69a2edecefb081908331ef8b9edf6636 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.