Triple
T32401391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mervyn's |
E827959
|
entity |
| Predicate | storeCountPeakApprox |
P160109
|
FINISHED |
| Object | over 250 stores |
—
|
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: over 250 stores | Statement: [Mervyn's, storeCountPeakApprox, over 250 stores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: storeCountPeakApprox Context triple: [Mervyn's, storeCountPeakApprox, over 250 stores]
-
A.
lapCountApproximate
Indicates that the recorded number of laps is an estimate rather than an exact, precise count.
-
B.
mineCountApproximate
Indicates that the number of mines associated with an entity is estimated or roughly counted rather than known exactly.
-
C.
prototypeCountApproximate
Indicates that the number of prototypes involved is an estimated or approximate count rather than an exact value.
-
D.
hasApproximateStoreCount
chosen
Indicates that an entity is associated with an estimated or approximate number of stores, rather than an exact count.
-
E.
hasPeakCount
Indicates the number of distinct peaks associated with an entity.
- 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_69f34919342c8190a4c3bf35a90d4e58 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6d16f5cb881908eed141afaaa0b51 |
completed | May 3, 2026, 4:39 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 12:52 a.m.